Chapter 4 Diversity analysis
4.1 Alpha diversity
# Calculate Hill numbers
richness <- genome_counts_filt %>%
column_to_rownames(var = "genome") %>%
dplyr::select(where(~ !all(. == 0))) %>%
hilldiv(., q = 0) %>%
t() %>%
as.data.frame() %>%
dplyr::rename(richness = 1) %>%
rownames_to_column(var = "sample")
neutral <- genome_counts_filt %>%
column_to_rownames(var = "genome") %>%
dplyr::select(where(~ !all(. == 0))) %>%
hilldiv(., q = 1) %>%
t() %>%
as.data.frame() %>%
dplyr::rename(neutral = 1) %>%
rownames_to_column(var = "sample")
phylogenetic <- genome_counts_filt %>%
column_to_rownames(var = "genome") %>%
dplyr::select(where(~ !all(. == 0))) %>%
hilldiv(., q = 1, tree = genome_tree) %>%
t() %>%
as.data.frame() %>%
dplyr::rename(phylogenetic = 1) %>%
rownames_to_column(var = "sample")
# Merge all metrics
alpha_div <- richness %>%
full_join(neutral, by = join_by(sample == sample)) %>%
full_join(phylogenetic, by = join_by(sample == sample))4.1.1 Acclimation samples
alpha_div %>%
pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="1_Acclimation") %>%
mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic"))) %>%
ggplot(aes(y = value, x = Population, group=Population, color=Population, fill=Population)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(alpha=0.5) +
scale_color_manual(name="Population",
breaks=c("Cold_wet","Hot_dry"),
labels=c("Cold","Hot"),
values=c('#008080', "#d57d2c")) +
scale_fill_manual(name="Population",
breaks=c("Cold_wet","Hot_dry"),
labels=c("Cold","Hot"),
values=c('#00808050', "#d57d2c50")) +
facet_wrap(. ~ metric,scales = "free") +
coord_cartesian(xlim = c(1, NA)) +
stat_compare_means(size=3, label.x=.58) +
theme_classic() +
theme(
strip.background = element_blank(),
panel.grid.minor.x = element_line(size = .1, color = "grey"),
axis.title.x = element_blank(),
axis.title.y = element_text(size=10),
axis.text.x = element_text(angle = 45, hjust = 1),
# Increase plot size
plot.title = element_text(size = 10),
axis.text = element_text(size = 8),
axis.title = element_text(size = 8)
) +
ylab("Alpha diversity")4.1.2 Antibiotics samples
alpha_div %>%
pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="2_Antibiotics") %>%
mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic"))) %>%
ggplot(aes(y = value, x = Population, group=Population, color=Population, fill=Population)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(alpha=0.5) +
scale_color_manual(name="Population",
breaks=c("Cold_wet","Hot_dry"),
labels=c("Cold","Hot"),
values=c('#008080', "#d57d2c")) +
scale_fill_manual(name="Population",
breaks=c("Cold_wet","Hot_dry"),
labels=c("Cold","Hot"),
values=c('#00808050', "#d57d2c50")) +
facet_wrap(. ~ metric,scales = "free") +
coord_cartesian(xlim = c(1, NA)) +
stat_compare_means(size=3, label.x=.58) +
theme_classic() +
theme(
strip.background = element_blank(),
panel.grid.minor.x = element_line(size = .1, color = "grey"),
axis.title.x = element_blank(),
axis.title.y = element_text(size=10),
axis.text.x = element_text(angle = 45, hjust = 1),
# Increase plot size
plot.title = element_text(size = 10),
axis.text = element_text(size = 8),
axis.title = element_text(size = 8)
) +
ylab("Alpha diversity")4.1.3 Transplant_1 samples
alpha_div %>%
pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="3_Transplant1") %>%
mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic"))) %>%
ggplot(aes(y = value, x = type, group=type, color=type, fill=type)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(alpha=0.5) +
scale_color_manual(name="Type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c", "#76b183")) +
scale_fill_manual(name="Type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
values=c("#4477AA50","#d57d2c50","#76b18350")) +
facet_wrap(. ~ metric,scales = "free") +
coord_cartesian(xlim = c(1, NA)) +
stat_compare_means(size=3, label.x=.7) +
theme_classic() +
theme(
strip.background = element_blank(),
panel.grid.minor.x = element_line(size = .1, color = "grey"),
axis.title.x = element_blank(),
axis.title.y = element_text(size=10),
axis.text.x = element_text(angle = 45, hjust = 1),
# Increase plot size
plot.title = element_text(size = 10),
axis.text = element_text(size = 8),
axis.title = element_text(size = 8)
) +
ylab("Alpha diversity")4.1.4 Transplant_2 samples
alpha_div %>%
pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="4_Transplant2") %>%
mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic"))) %>%
ggplot(aes(y = value, x = type, group=type, color=type, fill=type)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(alpha=0.5) +
scale_color_manual(name="Type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c", "#76b183")) +
scale_fill_manual(name="Type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
values=c("#4477AA50","#d57d2c50","#76b18350")) +
facet_wrap(. ~ metric,scales = "free") +
coord_cartesian(xlim = c(1, NA)) +
stat_compare_means(size=3, label.x=.7) +
theme_classic() +
theme(
strip.background = element_blank(),
panel.grid.minor.x = element_line(size = .1, color = "grey"),
axis.title.x = element_blank(),
axis.title.y = element_text(size=10),
axis.text.x = element_text(angle = 45, hjust = 1),
# Increase plot size
plot.title = element_text(size = 10),
axis.text = element_text(size = 8),
axis.title = element_text(size = 8)
) +
ylab("Alpha diversity")4.1.5 Post-Transplant_1 samples
alpha_div %>%
pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="5_Post-FMT1") %>%
mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic"))) %>%
ggplot(aes(y = value, x = type, group=type, color=type, fill=type)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(alpha=0.5) +
scale_color_manual(name="Type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c", "#76b183")) +
scale_fill_manual(name="Type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
values=c("#4477AA50","#d57d2c50","#76b18350")) +
facet_wrap(. ~ metric,scales = "free") +
coord_cartesian(xlim = c(1, NA)) +
stat_compare_means(size=3, label.x=.7) +
theme_classic() +
theme(
strip.background = element_blank(),
panel.grid.minor.x = element_line(size = .1, color = "grey"),
axis.title.x = element_blank(),
axis.title.y = element_text(size=10),
axis.text.x = element_text(angle = 45, hjust = 1),
# Increase plot size
plot.title = element_text(size = 10),
axis.text = element_text(size = 8),
axis.title = element_text(size = 8)
) +
ylab("Alpha diversity")4.1.6 Post-Transplant_2 samples
alpha_div %>%
pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
filter(time_point=="6_Post-FMT2") %>%
mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic"))) %>%
ggplot(aes(y = value, x = type, group=type, color=type, fill=type)) +
geom_boxplot(outlier.shape = NA) +
geom_jitter(alpha=0.5) +
scale_color_manual(name="Type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-control","Warm-control", "Cold-intervention"),
values=c("#4477AA","#d57d2c", "#76b183")) +
scale_fill_manual(name="Type",
breaks=c("Control","Hot_control", "Treatment"),
labels=c("Cold-control","Warm-control", "Cold-intervention"),
values=c("#4477AA50","#d57d2c50","#76b18350")) +
facet_wrap(. ~ metric,scales = "free") +
coord_cartesian(xlim = c(1, NA)) +
stat_compare_means(size=3, label.x=.7) +
theme_classic() +
theme(
strip.background = element_blank(),
panel.grid.minor.x = element_line(size = .1, color = "grey"),
axis.title.x = element_blank(),
axis.title.y = element_text(size=10),
axis.text.x = element_text(angle = 45, hjust = 1),
# Increase plot size
plot.title = element_text(size = 10),
axis.text = element_text(size = 8),
axis.title = element_text(size = 8)
) +
ylab("Alpha diversity")4.3 Permanovas
4.3.1 1. Do the antibiotics work?
4.3.1.1 Antibiotics
treat1 <- meta %>%
filter(time_point == "2_Antibiotics")
temp_genome_counts <- transform(genome_counts_filt, row.names = genome_counts_filt$genome)
temp_genome_counts$genome <- NULL
treat1.counts <- temp_genome_counts[,which(colnames(temp_genome_counts) %in% rownames(treat1))]
identical(sort(colnames(treat1.counts)),sort(as.character(rownames(treat1))))
treat1_nmds <- sample_metadata %>%
filter(time_point == "2_Antibiotics")4.3.1.1.2 Richness
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.015319 0.0153186 6.8764 999 0.02 *
Residuals 21 0.046782 0.0022277
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.02
Hot_dry 0.015919
adonis2(formula=beta_div_richness_treat1$S ~ Population, data=treat1[labels(beta_div_neutral_treat1$S),], permutations=999) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 1 | 1.356644 | 0.1527052 | 3.784762 | 0.001 |
| Residual | 21 | 7.527429 | 0.8472948 | NA | NA |
| Total | 22 | 8.884073 | 1.0000000 | NA | NA |
4.3.1.1.3 Neutral
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.030536 0.0305358 3.8593 999 0.076 .
Residuals 21 0.166158 0.0079123
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.073
Hot_dry 0.062842
adonis2(formula=beta_div_neutral_treat1$S ~ Population, data=treat1[labels(beta_div_neutral_treat1$S),], permutations=999) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 1 | 1.785669 | 0.2085055 | 5.532084 | 0.001 |
| Residual | 21 | 6.778468 | 0.7914945 | NA | NA |
| Total | 22 | 8.564137 | 1.0000000 | NA | NA |
4.3.1.1.4 Phylogenetic
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.012041 0.012041 0.9898 999 0.313
Residuals 21 0.255459 0.012165
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Cold_wet Hot_dry
Cold_wet 0.308
Hot_dry 0.33111
adonis2(formula=beta_div_phylo_treat1$S ~ Population, data=treat1[labels(beta_div_phylo_treat1$S),], permutations=999) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 1 | 0.8963254 | 0.1888758 | 4.889993 | 0.001 |
| Residual | 21 | 3.8492558 | 0.8111242 | NA | NA |
| Total | 22 | 4.7455811 | 1.0000000 | NA | NA |
beta_richness_nmds_treat1 <- beta_div_richness_treat1$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(treat1_nmds, by = c("sample" = "Tube_code"))
beta_neutral_nmds_treat1 <- beta_div_neutral_treat1$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(treat1_nmds, by = c("sample" = "Tube_code"))
beta_phylo_nmds_treat1 <- beta_div_phylo_treat1$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(treat1_nmds, by = join_by(sample == Tube_code))4.3.1.2 Acclimation vs antibiotics
treat <- meta %>%
filter(time_point == "1_Acclimation" | time_point == "2_Antibiotics")
temp_genome_counts <- transform(genome_counts_filt, row.names = genome_counts_filt$genome)
temp_genome_counts$genome <- NULL
treat.counts <- temp_genome_counts[,which(colnames(temp_genome_counts) %in% rownames(treat))]
identical(sort(colnames(treat.counts)),sort(as.character(rownames(treat))))
treat_nmds <- sample_metadata %>%
filter(time_point == "1_Acclimation" | time_point == "2_Antibiotics")4.3.1.2.1 Number of samples used
[1] 50
4.3.1.2.2 Richness
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.025318 0.0253178 6.021 999 0.021 *
Residuals 48 0.201837 0.0042049
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
1_Acclimation 2_Antibiotics
1_Acclimation 0.022
2_Antibiotics 0.017817
adonis2(formula=beta_div_richness_treat$S ~ time_point*Population, data=treat[labels(beta_div_neutral_treat$S),], permutations=999,strata=treat$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 3 | 4.885035 | 0.2455889 | 4.991572 | 0.001 |
| Residual | 46 | 15.006068 | 0.7544111 | NA | NA |
| Total | 49 | 19.891103 | 1.0000000 | NA | NA |
pairwise <- pairwise.adonis(beta_div_richness_treat$S, treat_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.3620815 | 1.0521088 | 0.06169963 | 0.334 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.2800877 | 4.6054436 | 0.22350616 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Control.2_Antibiotics | 1 | 1.0114074 | 2.8299035 | 0.15871670 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Treatment.2_Antibiotics | 1 | 0.9204347 | 2.4632144 | 0.14961929 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Hot_control.2_Antibiotics | 1 | 1.3738469 | 4.0051127 | 0.21073870 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.3606630 | 5.0871520 | 0.24124415 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Control.2_Antibiotics | 1 | 0.9707039 | 2.8037141 | 0.15747917 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Treatment.2_Antibiotics | 1 | 0.8277738 | 2.2885999 | 0.14050317 | 0.005 | 0.075 | |
| Treatment.1_Acclimation vs Hot_control.2_Antibiotics | 1 | 1.4551542 | 4.3850542 | 0.22620799 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Control.2_Antibiotics | 1 | 1.7346072 | 6.2936958 | 0.29556615 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Treatment.2_Antibiotics | 1 | 1.5634787 | 5.4659371 | 0.28079496 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Hot_control.2_Antibiotics | 1 | 1.2328439 | 4.7193158 | 0.23932452 | 0.002 | 0.030 | . |
| Control.2_Antibiotics vs Treatment.2_Antibiotics | 1 | 0.3007616 | 0.7949621 | 0.05762698 | 0.783 | 1.000 | |
| Control.2_Antibiotics vs Hot_control.2_Antibiotics | 1 | 1.0978513 | 3.1806716 | 0.18513081 | 0.001 | 0.015 | . |
| Treatment.2_Antibiotics vs Hot_control.2_Antibiotics | 1 | 1.0695525 | 2.9566393 | 0.18529210 | 0.001 | 0.015 | . |
4.3.1.2.3 Neutral
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.039587 0.039587 6.8387 999 0.009 **
Residuals 48 0.277854 0.005789
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
1_Acclimation 2_Antibiotics
1_Acclimation 0.014
2_Antibiotics 0.011886
adonis2(formula=beta_div_neutral_treat$S ~ time_point*Population, data=treat[labels(beta_div_neutral_treat$S),], permutations=999,strata=treat$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 3 | 5.756853 | 0.3024978 | 6.649871 | 0.001 |
| Residual | 46 | 13.274204 | 0.6975022 | NA | NA |
| Total | 49 | 19.031057 | 1.0000000 | NA | NA |
pairwise <- pairwise.adonis(beta_div_neutral_treat$S, treat_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.2316020 | 0.7712905 | 0.04598874 | 0.719 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.4015347 | 5.7562378 | 0.26457873 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Control.2_Antibiotics | 1 | 1.0524088 | 3.2499157 | 0.17807839 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Treatment.2_Antibiotics | 1 | 1.1115532 | 3.4118917 | 0.19595181 | 0.002 | 0.030 | . |
| Control.1_Acclimation vs Hot_control.2_Antibiotics | 1 | 1.7659070 | 6.0767588 | 0.28831562 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.6347704 | 6.8326887 | 0.29925029 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Control.2_Antibiotics | 1 | 1.0258101 | 3.2124718 | 0.17638857 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Treatment.2_Antibiotics | 1 | 0.8812272 | 2.7455916 | 0.16395907 | 0.002 | 0.030 | . |
| Treatment.1_Acclimation vs Hot_control.2_Antibiotics | 1 | 1.8759542 | 6.5571073 | 0.30417380 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Control.2_Antibiotics | 1 | 1.8018302 | 6.9639403 | 0.31706243 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Treatment.2_Antibiotics | 1 | 1.7960588 | 7.0145016 | 0.33379338 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Hot_control.2_Antibiotics | 1 | 1.1400941 | 5.0556070 | 0.25207948 | 0.001 | 0.015 | . |
| Control.2_Antibiotics vs Treatment.2_Antibiotics | 1 | 0.3090918 | 0.8838203 | 0.06365829 | 0.545 | 1.000 | |
| Control.2_Antibiotics vs Hot_control.2_Antibiotics | 1 | 1.3183156 | 4.2483600 | 0.23280777 | 0.001 | 0.015 | . |
| Treatment.2_Antibiotics vs Hot_control.2_Antibiotics | 1 | 1.4966720 | 4.8065093 | 0.26992990 | 0.002 | 0.030 | . |
4.3.1.2.4 Phylogenetic
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.58372 0.58372 35.413 999 0.001 ***
Residuals 48 0.79119 0.01648
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
1_Acclimation 2_Antibiotics
1_Acclimation 0.001
2_Antibiotics 2.9795e-07
adonis2(formula=beta_div_phylo_treat$S ~ time_point*Population, data=treat[labels(beta_div_phylo_treat$S),], permutations=999,strata=treat$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 3 | 2.947011 | 0.344846 | 8.070832 | 0.001 |
| Residual | 46 | 5.598866 | 0.655154 | NA | NA |
| Total | 49 | 8.545877 | 1.000000 | NA | NA |
pairwise <- pairwise.adonis(beta_div_phylo_treat$S, treat_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.04186923 | 0.4391642 | 0.02671451 | 0.733 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.15609416 | 2.5546889 | 0.13768428 | 0.053 | 0.795 | |
| Control.1_Acclimation vs Control.2_Antibiotics | 1 | 0.86133708 | 5.6599878 | 0.27395891 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Treatment.2_Antibiotics | 1 | 0.91317327 | 6.5682162 | 0.31933815 | 0.002 | 0.030 | . |
| Control.1_Acclimation vs Hot_control.2_Antibiotics | 1 | 0.70006680 | 5.4923385 | 0.26801912 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.23108846 | 4.0521838 | 0.20208192 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Control.2_Antibiotics | 1 | 0.69687259 | 4.7138353 | 0.23911305 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Treatment.2_Antibiotics | 1 | 0.68913760 | 5.1284906 | 0.26810744 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.2_Antibiotics | 1 | 0.59659295 | 4.8457011 | 0.24416880 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Control.2_Antibiotics | 1 | 1.09724077 | 9.8570170 | 0.39654867 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Treatment.2_Antibiotics | 1 | 1.22358928 | 12.8466332 | 0.47851934 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Hot_control.2_Antibiotics | 1 | 0.66150542 | 7.6387977 | 0.33742064 | 0.001 | 0.015 | . |
| Control.2_Antibiotics vs Treatment.2_Antibiotics | 1 | 0.09408368 | 0.4635496 | 0.03442997 | 0.885 | 1.000 | |
| Control.2_Antibiotics vs Hot_control.2_Antibiotics | 1 | 0.63116074 | 3.3932653 | 0.19509076 | 0.002 | 0.030 | . |
| Treatment.2_Antibiotics vs Hot_control.2_Antibiotics | 1 | 0.75130538 | 4.3068752 | 0.24885343 | 0.001 | 0.015 | . |
beta_richness_nmds_treat <- beta_div_richness_treat$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(treat_nmds, by = c("sample" = "Tube_code"))
beta_neutral_nmds_treat <- beta_div_neutral_treat$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(treat_nmds, by = c("sample" = "Tube_code"))
beta_phylo_nmds_treat <- beta_div_phylo_treat$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(treat_nmds, by = join_by(sample == Tube_code))4.3.2 2. Does the FMT work?
4.3.2.1 Comparison between FMT1 and FMT2
#Create newID to identify duplicated samples
transplants_metadata<-sample_metadata%>%
mutate(Tube_code=str_remove_all(Tube_code, "_a"))
transplants_metadata$newID <- paste(transplants_metadata$Tube_code, "_", transplants_metadata$individual)
transplant_all<-transplants_metadata%>%
filter(time_point == "3_Transplant1" | time_point == "4_Transplant2")%>%
filter(Tube_code != "AD45"| Tube_code != "AD48") %>%
column_to_rownames("newID")
transplant_all_nmds <- transplants_metadata %>%
filter(time_point == "3_Transplant1" | time_point == "4_Transplant2")%>%
filter(Tube_code != "AD45"| Tube_code != "AD48")
full_counts<-temp_genome_counts %>%
t()%>%
as.data.frame()%>%
rownames_to_column("Tube_code")%>%
full_join(transplants_metadata,by = join_by(Tube_code == Tube_code))
transplant_all_counts<-full_counts %>%
filter(time_point == "3_Transplant1" | time_point == "4_Transplant2") %>%
subset(select=-c(315:324)) %>%
column_to_rownames("newID")%>%
subset(select=-c(1))%>%
t() %>%
as.data.frame() %>%
mutate_if(is.character, as.numeric) %>%
subset(select=-c(47:48))
identical(sort(colnames(transplant_all_counts)),sort(as.character(rownames(transplant_all))))4.3.2.2 Number of samples used
[1] 48
beta_div_richness_transplant_all<-hillpair(data=transplant_all_counts, q=0)
beta_div_neutral_transplant_all<-hillpair(data=transplant_all_counts, q=1)
beta_div_phylo_transplant_all<-hillpair(data=transplant_all_counts, q=1, tree=genome_tree)#Arrange of metadata dataframe
transplant_all_arrange<-transplant_all[labels(beta_div_neutral_transplant_all$S),]
transplant_all_arrange$type_time <- interaction(transplant_all_arrange$type, transplant_all_arrange$time_point)4.3.2.2.1 Richness
adonis2(formula=beta_div_richness_transplant_all$S ~ time_point*type, data=transplant_all[labels(beta_div_richness_transplant_all$S),], permutations=999) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 5 | 3.636954 | 0.3294321 | 3.930187 | 0.001 |
| Residual | 40 | 7.403117 | 0.6705679 | NA | NA |
| Total | 45 | 11.040070 | 1.0000000 | NA | NA |
pairwise<-pairwise.adonis(beta_div_richness_transplant_all$S,transplant_all_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.3_Transplant1 vs Treatment.3_Transplant1 | 1 | 1.129362662 | 5.79950801 | 0.292911723 | 0.001 | 0.015 | . |
| Control.3_Transplant1 vs Hot_control.3_Transplant1 | 1 | 1.208865861 | 6.33395382 | 0.296895450 | 0.001 | 0.015 | . |
| Control.3_Transplant1 vs Control.4_Transplant2 | 1 | 0.131647583 | 0.64704882 | 0.038868680 | 0.860 | 1.000 | |
| Control.3_Transplant1 vs Treatment.4_Transplant2 | 1 | 1.120646022 | 5.84730567 | 0.310246237 | 0.002 | 0.030 | . |
| Control.3_Transplant1 vs Hot_control.4_Transplant2 | 1 | 1.223919777 | 6.45841493 | 0.315685010 | 0.001 | 0.015 | . |
| Treatment.3_Transplant1 vs Hot_control.3_Transplant1 | 1 | 0.009102921 | 0.05153754 | 0.003948772 | 1.000 | 1.000 | |
| Treatment.3_Transplant1 vs Control.4_Transplant2 | 1 | 1.314300451 | 6.84362433 | 0.328331782 | 0.001 | 0.015 | . |
| Treatment.3_Transplant1 vs Treatment.4_Transplant2 | 1 | 0.074927876 | 0.42820379 | 0.037469037 | 0.975 | 1.000 | |
| Treatment.3_Transplant1 vs Hot_control.4_Transplant2 | 1 | 0.077903563 | 0.44805710 | 0.035994139 | 0.963 | 1.000 | |
| Hot_control.3_Transplant1 vs Control.4_Transplant2 | 1 | 1.410186386 | 7.48716456 | 0.332952807 | 0.001 | 0.015 | . |
| Hot_control.3_Transplant1 vs Treatment.4_Transplant2 | 1 | 0.077823551 | 0.45304555 | 0.036380301 | 0.965 | 1.000 | |
| Hot_control.3_Transplant1 vs Hot_control.4_Transplant2 | 1 | 0.067817527 | 0.39659721 | 0.029604324 | 0.985 | 1.000 | |
| Control.4_Transplant2 vs Treatment.4_Transplant2 | 1 | 1.248404869 | 6.61377735 | 0.337200593 | 0.001 | 0.015 | . |
| Control.4_Transplant2 vs Hot_control.4_Transplant2 | 1 | 1.357472348 | 7.26616675 | 0.341677315 | 0.001 | 0.015 | . |
| Treatment.4_Transplant2 vs Hot_control.4_Transplant2 | 1 | 0.012433593 | 0.07386441 | 0.006670157 | 1.000 | 1.000 |
4.3.2.2.2 Neutral
adonis2(formula=beta_div_neutral_transplant_all$S ~ time_point*type, data=transplant_all[labels(beta_div_neutral_transplant_all$S),], permutations=999) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 5 | 3.888690 | 0.3472704 | 4.256223 | 0.001 |
| Residual | 40 | 7.309185 | 0.6527296 | NA | NA |
| Total | 45 | 11.197875 | 1.0000000 | NA | NA |
pairwise<-pairwise.adonis(beta_div_neutral_transplant_all$S,transplant_all_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.3_Transplant1 vs Treatment.3_Transplant1 | 1 | 1.210492327 | 6.33244693 | 0.311445393 | 0.001 | 0.015 | . |
| Control.3_Transplant1 vs Hot_control.3_Transplant1 | 1 | 1.329816668 | 7.07170798 | 0.320396953 | 0.001 | 0.015 | . |
| Control.3_Transplant1 vs Control.4_Transplant2 | 1 | 0.095527891 | 0.51782131 | 0.031349250 | 0.911 | 1.000 | |
| Control.3_Transplant1 vs Treatment.4_Transplant2 | 1 | 1.202957055 | 6.54398303 | 0.334833643 | 0.001 | 0.015 | . |
| Control.3_Transplant1 vs Hot_control.4_Transplant2 | 1 | 1.336430279 | 7.33775774 | 0.343886074 | 0.001 | 0.015 | . |
| Treatment.3_Transplant1 vs Hot_control.3_Transplant1 | 1 | 0.009634139 | 0.05096559 | 0.003905121 | 1.000 | 1.000 | |
| Treatment.3_Transplant1 vs Control.4_Transplant2 | 1 | 1.400930696 | 7.57729789 | 0.351169916 | 0.001 | 0.015 | . |
| Treatment.3_Transplant1 vs Treatment.4_Transplant2 | 1 | 0.070676505 | 0.38364693 | 0.033701584 | 0.918 | 1.000 | |
| Treatment.3_Transplant1 vs Hot_control.4_Transplant2 | 1 | 0.078300724 | 0.42972488 | 0.034572356 | 0.898 | 1.000 | |
| Hot_control.3_Transplant1 vs Control.4_Transplant2 | 1 | 1.529217161 | 8.39335845 | 0.358792367 | 0.001 | 0.015 | . |
| Hot_control.3_Transplant1 vs Treatment.4_Transplant2 | 1 | 0.067325622 | 0.37214249 | 0.030079066 | 0.920 | 1.000 | |
| Hot_control.3_Transplant1 vs Hot_control.4_Transplant2 | 1 | 0.060700684 | 0.33852115 | 0.025379211 | 0.937 | 1.000 | |
| Control.4_Transplant2 vs Treatment.4_Transplant2 | 1 | 1.318966989 | 7.44875581 | 0.364264500 | 0.001 | 0.015 | . |
| Control.4_Transplant2 vs Hot_control.4_Transplant2 | 1 | 1.461842335 | 8.31259383 | 0.372551658 | 0.001 | 0.015 | . |
| Treatment.4_Transplant2 vs Hot_control.4_Transplant2 | 1 | 0.012314222 | 0.07128989 | 0.006439167 | 1.000 | 1.000 |
4.3.2.2.3 Phylogenetic
adonis2(formula=beta_div_phylo_transplant_all$S ~ time_point*type, data=transplant_all[labels(beta_div_phylo_transplant_all$S),], permutations=999) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 5 | 0.3748882 | 0.2242933 | 2.313177 | 0.008 |
| Residual | 40 | 1.2965312 | 0.7757067 | NA | NA |
| Total | 45 | 1.6714194 | 1.0000000 | NA | NA |
pairwise<-pairwise.adonis(beta_div_phylo_transplant_all$S,transplant_all_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.3_Transplant1 vs Treatment.3_Transplant1 | 1 | 0.098602035 | 2.40012240 | 0.146347835 | 0.049 | 0.735 | |
| Control.3_Transplant1 vs Hot_control.3_Transplant1 | 1 | 0.111980145 | 2.78142890 | 0.156423250 | 0.013 | 0.195 | |
| Control.3_Transplant1 vs Control.4_Transplant2 | 1 | 0.007928377 | 0.35768341 | 0.021866385 | 0.910 | 1.000 | |
| Control.3_Transplant1 vs Treatment.4_Transplant2 | 1 | 0.107460204 | 3.49439587 | 0.211853523 | 0.014 | 0.210 | |
| Control.3_Transplant1 vs Hot_control.4_Transplant2 | 1 | 0.123295950 | 4.08042827 | 0.225682058 | 0.002 | 0.030 | . |
| Treatment.3_Transplant1 vs Hot_control.3_Transplant1 | 1 | 0.001916654 | 0.03838869 | 0.002944282 | 0.986 | 1.000 | |
| Treatment.3_Transplant1 vs Control.4_Transplant2 | 1 | 0.138394849 | 4.84607372 | 0.257139699 | 0.001 | 0.015 | . |
| Treatment.3_Transplant1 vs Treatment.4_Transplant2 | 1 | 0.022693131 | 0.56103262 | 0.048527899 | 0.626 | 1.000 | |
| Treatment.3_Transplant1 vs Hot_control.4_Transplant2 | 1 | 0.016177953 | 0.41465150 | 0.033400172 | 0.760 | 1.000 | |
| Hot_control.3_Transplant1 vs Control.4_Transplant2 | 1 | 0.151529980 | 5.30364409 | 0.261216364 | 0.002 | 0.030 | . |
| Hot_control.3_Transplant1 vs Treatment.4_Transplant2 | 1 | 0.018543667 | 0.46976985 | 0.037672696 | 0.721 | 1.000 | |
| Hot_control.3_Transplant1 vs Hot_control.4_Transplant2 | 1 | 0.011324236 | 0.29624361 | 0.022280248 | 0.824 | 1.000 | |
| Control.4_Transplant2 vs Treatment.4_Transplant2 | 1 | 0.123072115 | 7.12848761 | 0.354149191 | 0.002 | 0.030 | . |
| Control.4_Transplant2 vs Hot_control.4_Transplant2 | 1 | 0.149801593 | 8.46696532 | 0.376862883 | 0.001 | 0.015 | . |
| Treatment.4_Transplant2 vs Hot_control.4_Transplant2 | 1 | 0.001788609 | 0.06719083 | 0.006071173 | 0.980 | 1.000 |
beta_richness_nmds_transplant_all <- beta_div_richness_transplant_all$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(transplant_all_nmds, by = join_by(sample == newID))
beta_neutral_nmds_transplant_all <- beta_div_neutral_transplant_all$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(transplant_all_nmds, by = join_by(sample == newID))
beta_phylo_nmds_transplant_all <- beta_div_phylo_transplant_all$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(transplant_all_nmds, by = join_by(sample == newID))p0<-beta_richness_nmds_transplant_all %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y = "NMDS2", x="NMDS1 \n Richness beta diversity") +
theme_classic() +
theme(legend.position="none")
p1<-beta_neutral_nmds_transplant_all %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y = "NMDS2", x="NMDS1 \n Neutral beta diversity") +
theme_classic() +
theme(legend.position="none")
p2<-beta_phylo_nmds_transplant_all %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y= element_blank (), x="NMDS1 \n Phylogenetic beta diversity") +
theme_classic() +
theme(legend.position="none")4.3.2.3 Comparison between FMT2 vs Post-FMT2
#Create newID to identify duplicated samples
transplants_metadata<-sample_metadata%>%
mutate(Tube_code=str_remove_all(Tube_code, "_a"))
transplants_metadata$newID <- paste(transplants_metadata$Tube_code, "_", transplants_metadata$individual)
transplant3<-transplants_metadata%>%
filter(time_point == "4_Transplant2" | time_point == "6_Post-FMT2")%>%
column_to_rownames("newID")
transplant3_nmds <- transplants_metadata %>%
filter(time_point == "4_Transplant2" | time_point == "6_Post-FMT2")
full_counts<-temp_genome_counts %>%
t()%>%
as.data.frame()%>%
rownames_to_column("Tube_code")%>%
full_join(transplants_metadata,by = join_by(Tube_code == Tube_code))
transplant3_counts<-full_counts %>%
filter(time_point == "4_Transplant2" | time_point == "6_Post-FMT2") %>%
subset(select=-c(315:324)) %>%
column_to_rownames("newID")%>%
subset(select=-c(1))%>%
t() %>%
as.data.frame() %>%
mutate_if(is.character, as.numeric)
identical(sort(colnames(transplant3_counts)),sort(as.character(rownames(transplant3))))4.3.2.4 Number of samples used
[1] 49
beta_div_richness_transplant3<-hillpair(data=transplant3_counts, q=0)
beta_div_neutral_transplant3<-hillpair(data=transplant3_counts, q=1)
beta_div_phylo_transplant3<-hillpair(data=transplant3_counts, q=1, tree=genome_tree)#Arrange of metadata dataframe
transplant3_arrange<-transplant3[labels(beta_div_neutral_transplant3$S),]4.3.2.4.1 Richness
adonis2(formula=beta_div_richness_transplant3$S ~ Population+time_point+type, data=transplant3[labels(beta_div_richness_transplant3$S),], permutations=999, strata=transplant3$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 3 | 3.500812 | 0.2535872 | 5.096117 | 0.001 |
| Residual | 45 | 10.304350 | 0.7464128 | NA | NA |
| Total | 48 | 13.805162 | 1.0000000 | NA | NA |
pairwise<-pairwise.adonis(beta_div_richness_transplant3$S,transplant3_arrange$type, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control vs Treatment | 1 | 1.4169018 | 5.739828 | 0.15622903 | 0.001 | 0.003 | * |
| Control vs Hot_control | 1 | 2.0940966 | 8.509112 | 0.21005427 | 0.001 | 0.003 | * |
| Treatment vs Hot_control | 1 | 0.3004618 | 1.265034 | 0.04179854 | 0.160 | 0.480 |
4.3.2.4.2 Neutral
adonis2(formula=beta_div_neutral_transplant3$S ~ Population+time_point+type, data=transplant3[labels(beta_div_neutral_transplant3$S),], permutations=999, strata=transplant3$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 3 | 4.128749 | 0.3031142 | 6.524331 | 0.001 |
| Residual | 45 | 9.492350 | 0.6968858 | NA | NA |
| Total | 48 | 13.621099 | 1.0000000 | NA | NA |
pairwise<-pairwise.adonis(beta_div_neutral_transplant3$S,transplant3_arrange$type, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control vs Treatment | 1 | 1.8758788 | 8.282671 | 0.21084796 | 0.001 | 0.003 | * |
| Control vs Hot_control | 1 | 2.4396317 | 10.635546 | 0.24945256 | 0.001 | 0.003 | * |
| Treatment vs Hot_control | 1 | 0.3158428 | 1.394345 | 0.04587515 | 0.116 | 0.348 |
4.3.2.4.3 Phylogenetic
adonis2(formula=beta_div_phylo_transplant3$S ~ Population+time_point+type, data=transplant3[labels(beta_div_phylo_transplant3$S),], permutations=999, strata=transplant3$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 3 | 0.3971179 | 0.2701357 | 5.551766 | 0.001 |
| Residual | 45 | 1.0729504 | 0.7298643 | NA | NA |
| Total | 48 | 1.4700683 | 1.0000000 | NA | NA |
pairwise<-pairwise.adonis(beta_div_phylo_transplant3$S,transplant3_arrange$type, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control vs Treatment | 1 | 0.14387705 | 5.735321 | 0.15612552 | 0.002 | 0.006 | * |
| Control vs Hot_control | 1 | 0.22715701 | 9.044894 | 0.22036587 | 0.001 | 0.003 | * |
| Treatment vs Hot_control | 1 | 0.04648319 | 1.704277 | 0.05550617 | 0.124 | 0.372 |
beta_richness_nmds_transplant3 <- beta_div_richness_transplant3$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(transplant3_nmds, by = join_by(sample == newID))
beta_neutral_nmds_transplant3 <- beta_div_neutral_transplant3$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(transplant3_nmds, by = join_by(sample == newID))
beta_phylo_nmds_transplant3 <- beta_div_phylo_transplant3$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(transplant3_nmds, by = join_by(sample == newID))p0<-beta_richness_nmds_transplant3 %>%
group_by(individual) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y = "NMDS2", x="NMDS1 \n Richness beta diversity") +
theme_classic() +
theme(legend.position="none")
p1<-beta_neutral_nmds_transplant3 %>%
group_by(individual) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y = "NMDS2", x="NMDS1 \n Neutral beta diversity") +
theme_classic() +
theme(legend.position="none")
p2<-beta_phylo_nmds_transplant3 %>%
group_by(individual) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y = "NMDS2", x="NMDS1 \n Phylogenetic beta diversity") +
theme_classic() +
theme(legend.position="none")4.3.2.5 Comparison between the different experimental time points (Acclimation vs Transplant samples)
The estimated time for calculating the 5151 pairwise combinations is 34 seconds.
4.3.2.6 Comparison of acclimation samples to transplant samples
transplant7<-transplants_metadata%>%
filter(time_point == "4_Transplant2" | time_point == "1_Acclimation"| time_point == "3_Transplant1")%>%
column_to_rownames("newID")
transplant7_nmds <- transplants_metadata %>%
filter(time_point == "4_Transplant2" | time_point == "1_Acclimation"| time_point == "3_Transplant1")
transplant7_counts<-full_counts %>%
filter(time_point == "4_Transplant2" | time_point == "1_Acclimation"| time_point == "3_Transplant1") %>%
subset(select=-c(315:324)) %>%
column_to_rownames("newID")%>%
subset(select=-c(1))%>%
t() %>%
as.data.frame() %>%
mutate_if(is.character, as.numeric)
transplant7_counts <- transplant7_counts[, !names(transplant7_counts) %in% c("AD45 _ LI1_2nd_2", "AD48 _ LI1_2nd_6")]
identical(sort(colnames(transplant7_counts)),sort(as.character(rownames(transplant7))))[1] TRUE
4.3.2.7 Number of samples used
[1] 73
beta_div_richness_transplant7<-hillpair(data=transplant7_counts, q=0)
beta_div_neutral_transplant7<-hillpair(data=transplant7_counts, q=1)
beta_div_phylo_transplant7<-hillpair(data=transplant7_counts, q=1, tree=genome_tree)#Arrange of metadata dataframe
transplant7_arrange<-transplant7[labels(beta_div_neutral_transplant7$S),]
transplant7_arrange <- transplant7_arrange %>%
mutate(time_point = recode(time_point,
"3_Transplant1" = "Transplant",
"4_Transplant2" = "Transplant"))
transplant7_arrange$type_time <- interaction(transplant7_arrange$type, transplant7_arrange$time_point)4.3.2.7.1 Richness
adonis2(formula=beta_div_richness_transplant7$S ~ Population*time_point+type, data=transplant7[labels(beta_div_richness_transplant7$S),], permutations=999, strata=transplant7$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 6 | 5.309519 | 0.2518733 | 3.703392 | 0.002 |
| Residual | 66 | 15.770599 | 0.7481267 | NA | NA |
| Total | 72 | 21.080119 | 1.0000000 | NA | NA |
pairwise<-pairwise.adonis(beta_div_richness_transplant7$S,transplant7_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.36208146 | 1.0521088 | 0.06169963 | 0.309 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.28008774 | 4.6054436 | 0.22350616 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Control.Transplant | 1 | 0.55038651 | 2.2107376 | 0.08124505 | 0.002 | 0.030 | . |
| Control.1_Acclimation vs Treatment.Transplant | 1 | 1.62289430 | 6.7106689 | 0.25123553 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Hot_control.Transplant | 1 | 1.73215888 | 7.4315069 | 0.25250175 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.36066298 | 5.0871520 | 0.24124415 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Control.Transplant | 1 | 0.52860586 | 2.1820402 | 0.08027507 | 0.002 | 0.030 | . |
| Treatment.1_Acclimation vs Treatment.Transplant | 1 | 1.76810026 | 7.5736721 | 0.27467042 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.Transplant | 1 | 1.87790626 | 8.3291875 | 0.27462613 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Control.Transplant | 1 | 1.75314247 | 8.7706781 | 0.25971282 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Treatment.Transplant | 1 | 0.27700454 | 1.5346880 | 0.07126586 | 0.091 | 1.000 | |
| Hot_control.1_Acclimation vs Hot_control.Transplant | 1 | 0.26448976 | 1.4916174 | 0.06349573 | 0.088 | 1.000 | |
| Control.Transplant vs Treatment.Transplant | 1 | 2.30884687 | 12.4299510 | 0.30002331 | 0.001 | 0.015 | . |
| Control.Transplant vs Hot_control.Transplant | 1 | 2.50396161 | 13.6713271 | 0.30604256 | 0.001 | 0.015 | . |
| Treatment.Transplant vs Hot_control.Transplant | 1 | 0.01688622 | 0.1023282 | 0.00392027 | 0.998 | 1.000 |
4.3.2.7.2 Neutral
adonis2(formula=beta_div_neutral_transplant7$S ~ Population+time_point*type, data=transplant7[labels(beta_div_neutral_transplant7$S),], permutations=999, strata=transplant7$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 8 | 7.284378 | 0.3492417 | 4.293351 | 0.001 |
| Residual | 64 | 13.573319 | 0.6507583 | NA | NA |
| Total | 72 | 20.857698 | 1.0000000 | NA | NA |
pairwise<-pairwise.adonis(beta_div_neutral_transplant7$S,transplant7_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.23160196 | 0.7712905 | 0.045988741 | 0.720 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.40153474 | 5.7562378 | 0.264578733 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Control.Transplant | 1 | 0.56111203 | 2.5583085 | 0.092832565 | 0.004 | 0.060 | |
| Control.1_Acclimation vs Treatment.Transplant | 1 | 1.88709838 | 8.3257794 | 0.293929402 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Hot_control.Transplant | 1 | 2.02585000 | 9.2317432 | 0.295588471 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.63477039 | 6.8326887 | 0.299250291 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Control.Transplant | 1 | 0.61335323 | 2.8313912 | 0.101733730 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Treatment.Transplant | 1 | 2.10939140 | 9.4473664 | 0.320822116 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.Transplant | 1 | 2.24827218 | 10.3907678 | 0.320794118 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Control.Transplant | 1 | 1.87351542 | 10.3925002 | 0.293635661 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Treatment.Transplant | 1 | 0.34276062 | 1.9273510 | 0.087897118 | 0.051 | 0.765 | |
| Hot_control.1_Acclimation vs Hot_control.Transplant | 1 | 0.31638309 | 1.8072337 | 0.075911118 | 0.072 | 1.000 | |
| Control.Transplant vs Treatment.Transplant | 1 | 2.48701901 | 14.0199769 | 0.325894571 | 0.001 | 0.015 | . |
| Control.Transplant vs Hot_control.Transplant | 1 | 2.75304261 | 15.6912860 | 0.336064549 | 0.001 | 0.015 | . |
| Treatment.Transplant vs Hot_control.Transplant | 1 | 0.01764676 | 0.1022118 | 0.003915827 | 0.992 | 1.000 |
4.3.2.7.3 Phylogenetic
adonis2(formula=beta_div_phylo_transplant7$S ~ Population+time_point+type, data=transplant7[labels(beta_div_phylo_transplant7$S),], permutations=999, strata=transplant7$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 4 | 0.7377029 | 0.1879202 | 3.933904 | 0.028 |
| Residual | 68 | 3.1879143 | 0.8120798 | NA | NA |
| Total | 72 | 3.9256172 | 1.0000000 | NA | NA |
pairwise<-pairwise.adonis(beta_div_phylo_transplant7$S,transplant7_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.04186923 | 0.43916424 | 0.026714511 | 0.720 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.15609416 | 2.55468892 | 0.137684276 | 0.030 | 0.450 | |
| Control.1_Acclimation vs Control.Transplant | 1 | 0.03888650 | 0.83961027 | 0.032493148 | 0.492 | 1.000 | |
| Control.1_Acclimation vs Treatment.Transplant | 1 | 0.28946588 | 4.58406811 | 0.186464994 | 0.002 | 0.030 | . |
| Control.1_Acclimation vs Hot_control.Transplant | 1 | 0.31864880 | 5.37781508 | 0.196429666 | 0.002 | 0.030 | . |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.23108846 | 4.05218385 | 0.202081922 | 0.002 | 0.030 | . |
| Treatment.1_Acclimation vs Control.Transplant | 1 | 0.11794420 | 2.69844074 | 0.097422117 | 0.030 | 0.450 | |
| Treatment.1_Acclimation vs Treatment.Transplant | 1 | 0.37640156 | 6.28511923 | 0.239113210 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.Transplant | 1 | 0.40433696 | 7.18306079 | 0.246138020 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Control.Transplant | 1 | 0.11597038 | 5.32063275 | 0.175478948 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Treatment.Transplant | 1 | 0.03673004 | 1.13023077 | 0.053488804 | 0.353 | 1.000 | |
| Hot_control.1_Acclimation vs Hot_control.Transplant | 1 | 0.04097680 | 1.30539166 | 0.056012432 | 0.266 | 1.000 | |
| Control.Transplant vs Treatment.Transplant | 1 | 0.21736741 | 7.59281199 | 0.207494630 | 0.001 | 0.015 | . |
| Control.Transplant vs Hot_control.Transplant | 1 | 0.25837791 | 9.19762187 | 0.228810100 | 0.001 | 0.015 | . |
| Treatment.Transplant vs Hot_control.Transplant | 1 | 0.00180330 | 0.04804393 | 0.001844435 | 0.968 | 1.000 |
beta_richness_nmds_transplant7 <- beta_div_richness_transplant7$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(transplant7_nmds, by = join_by(sample == newID))
beta_neutral_nmds_transplant7 <- beta_div_neutral_transplant7$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(transplant7_nmds, by = join_by(sample == newID))
beta_phylo_nmds_transplant7 <- beta_div_phylo_transplant7$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(transplant7_nmds, by = join_by(sample == newID))p0<-beta_richness_nmds_transplant7 %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
scale_shape_manual(name="time_point",
breaks=c("1_Acclimation", "3_Transplant1", "4_Transplant2"),
labels=c("Acclimation", "Transplant", "Transplant"),
values=c("circle","square","square")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y = "NMDS2", x="NMDS1 \n Richness beta diversity") +
theme_classic() +
theme(legend.position="none")
p1<-beta_neutral_nmds_transplant7 %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
scale_shape_manual(name="time_point",
breaks=c("1_Acclimation", "3_Transplant1", "4_Transplant2"),
labels=c("Acclimation", "Transplant", "Transplant"),
values=c("circle","square","square")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y= element_blank (), x="NMDS1 \n Neutral beta diversity") +
theme_classic() +
theme(legend.position="none")
p2<-beta_phylo_nmds_transplant7 %>%
group_by(type) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
scale_color_manual(name="Type",
breaks=c("Control", "Hot_control", "Treatment"),
labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
values=c("#4477AA","#d57d2c","#76b183")) +
scale_shape_manual(name="time_point",
breaks=c("1_Acclimation", "3_Transplant1", "4_Transplant2"),
labels=c("Acclimation", "Transplant", "Transplant"),
values=c("circle","square","square")) +
geom_point(size=2) +
geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
labs(y= element_blank (), x="NMDS1 \n Phylogenetic beta diversity") +
theme_classic() +
theme(legend.position="none")4.3.2.8 Comparison between Acclimation vs Post-FMT1
post3 <- meta %>%
filter(time_point == "1_Acclimation" | time_point == "5_Post-FMT1")
temp_genome_counts <- transform(genome_counts_filt, row.names = genome_counts_filt$genome)
temp_genome_counts$genome <- NULL
post3.counts <- temp_genome_counts[,which(colnames(temp_genome_counts) %in% rownames(post3))]
identical(sort(colnames(post3.counts)),sort(as.character(rownames(post3))))
post3_nmds <- sample_metadata %>%
filter(time_point == "1_Acclimation" | time_point == "5_Post-FMT1")4.3.2.9 Number of samples used
[1] 53
beta_div_richness_post3<-hillpair(data=post3.counts, q=0)
beta_div_neutral_post3<-hillpair(data=post3.counts, q=1)
beta_div_phylo_post3<-hillpair(data=post3.counts, q=1, tree=genome_tree)#Arrange of metadata dataframe
post3_arrange<-post3[labels(beta_div_neutral_post3$S),]
post3_arrange$type_time <- interaction(post3_arrange$type, post3_arrange$time_point)4.3.2.9.1 Richness
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.099607 0.049803 9.5441 999 0.001 ***
Residuals 50 0.260911 0.005218
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.00100000 0.897
Hot_control 0.00102653 0.001
Treatment 0.88832670 0.00010131
adonis2(formula=beta_div_richness_post3$S ~ time_point*Population, data=post3[labels(beta_div_neutral_post3$S),], permutations=999,strata=post3$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 3 | 3.479739 | 0.1872879 | 3.763983 | 0.001 |
| Residual | 49 | 15.099892 | 0.8127121 | NA | NA |
| Total | 52 | 18.579631 | 1.0000000 | NA | NA |
pairwise <- pairwise.adonis(beta_div_richness_post3$S, post3_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.3620815 | 1.052109 | 0.06169963 | 0.330 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.2800877 | 4.605444 | 0.22350616 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.6845657 | 1.998114 | 0.11101796 | 0.002 | 0.030 | . |
| Control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.8437461 | 2.499232 | 0.14281954 | 0.002 | 0.030 | . |
| Control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 1.1208022 | 3.568670 | 0.18236649 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.3606630 | 5.087152 | 0.24124415 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.7216200 | 2.172734 | 0.11956009 | 0.002 | 0.030 | . |
| Treatment.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.9551308 | 2.926054 | 0.16322910 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 1.2263345 | 4.039487 | 0.20157637 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 1.4319792 | 5.384836 | 0.25180628 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.8172413 | 3.194690 | 0.17558364 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.5796135 | 2.441615 | 0.13239702 | 0.001 | 0.015 | . |
| Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 | 1 | 0.5615418 | 1.729004 | 0.10335366 | 0.015 | 0.225 | |
| Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.8438429 | 2.793772 | 0.14865413 | 0.001 | 0.015 | . |
| Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.3734921 | 1.268929 | 0.07799710 | 0.116 | 1.000 |
4.3.2.9.2 Neutral
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.00945 0.0094472 1.1428 999 0.264
Residuals 51 0.42161 0.0082669
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
1_Acclimation 5_Post-FMT1
1_Acclimation 0.267
5_Post-FMT1 0.2901
adonis2(formula=beta_div_neutral_post3$S ~ time_point*Population, data=post3[labels(beta_div_neutral_post3$S),], permutations=999,strata=post3$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 3 | 4.465574 | 0.2549304 | 5.588555 | 0.001 |
| Residual | 49 | 13.051264 | 0.7450696 | NA | NA |
| Total | 52 | 17.516838 | 1.0000000 | NA | NA |
pairwise <- pairwise.adonis(beta_div_neutral_post3$S, post3_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.2316020 | 0.7712905 | 0.04598874 | 0.733 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.4015347 | 5.7562378 | 0.26457873 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.8332162 | 2.9081103 | 0.15380227 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 1.1719595 | 4.0685514 | 0.21336447 | 0.002 | 0.030 | . |
| Control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 1.4260875 | 5.2413171 | 0.24675104 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.6347704 | 6.8326887 | 0.29925029 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.9517634 | 3.3715700 | 0.17404733 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 1.3127773 | 4.6298256 | 0.23585668 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 1.6713369 | 6.2395460 | 0.28056085 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 1.5409781 | 6.8338056 | 0.29928456 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.9133614 | 4.0964534 | 0.21451383 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.6954835 | 3.2951234 | 0.17077493 | 0.001 | 0.015 | . |
| Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 | 1 | 0.6051778 | 2.2508491 | 0.13047758 | 0.014 | 0.210 | |
| Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 1.0528902 | 4.1436369 | 0.20570451 | 0.001 | 0.015 | . |
| Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.4150076 | 1.6372683 | 0.09840968 | 0.056 | 0.840 |
4.3.2.9.3 Phylogenetic
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.05132 0.051320 2.6745 999 0.085 .
Residuals 51 0.97861 0.019189
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
1_Acclimation 5_Post-FMT1
1_Acclimation 0.091
5_Post-FMT1 0.10812
adonis2(formula=beta_div_phylo_post3$S ~ time_point*Population, data=post3[labels(beta_div_phylo_post3$S),], permutations=999,strata=post3$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 3 | 0.7332141 | 0.2105602 | 4.356444 | 0.001 |
| Residual | 49 | 2.7489923 | 0.7894398 | NA | NA |
| Total | 52 | 3.4822065 | 1.0000000 | NA | NA |
pairwise <- pairwise.adonis(beta_div_phylo_post3$S, post3_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.04186923 | 0.4391642 | 0.02671451 | 0.731 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.15609416 | 2.5546889 | 0.13768428 | 0.034 | 0.510 | |
| Control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.19193367 | 2.9749922 | 0.15678490 | 0.021 | 0.315 | |
| Control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.14627288 | 1.7907381 | 0.10665035 | 0.152 | 1.000 | |
| Control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.25061348 | 3.6146185 | 0.18428187 | 0.009 | 0.135 | |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.23108846 | 4.0521838 | 0.20208192 | 0.002 | 0.030 | . |
| Treatment.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.26358465 | 4.3608960 | 0.21417997 | 0.009 | 0.135 | |
| Treatment.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.25319427 | 3.2738422 | 0.17915456 | 0.040 | 0.600 | |
| Treatment.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.39050120 | 5.9837393 | 0.27218933 | 0.002 | 0.030 | . |
| Hot_control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.14203376 | 5.4200212 | 0.25303529 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.09666753 | 2.3682173 | 0.13635351 | 0.023 | 0.345 | |
| Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.09252600 | 2.9824958 | 0.15711821 | 0.005 | 0.075 | |
| Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 | 1 | 0.01842535 | 0.4144162 | 0.02688498 | 0.769 | 1.000 | |
| Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.05987967 | 1.7387847 | 0.09802164 | 0.117 | 1.000 | |
| Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.03212966 | 0.6477782 | 0.04139746 | 0.674 | 1.000 |
beta_richness_nmds_post3 <- beta_div_richness_post3$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post3_nmds, by = c("sample" = "Tube_code"))
beta_neutral_nmds_post3 <- beta_div_neutral_post3$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post3_nmds, by = c("sample" = "Tube_code"))
beta_phylo_nmds_post3 <- beta_div_phylo_post3$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post3_nmds, by = join_by(sample == Tube_code))4.3.2.10 Comparison between Acclimation vs Post-FMT2
post4 <- meta %>%
filter(time_point == "1_Acclimation" | time_point == "6_Post-FMT2")
temp_genome_counts <- transform(genome_counts_filt, row.names = genome_counts_filt$genome)
temp_genome_counts$genome <- NULL
post4.counts <- temp_genome_counts[,which(colnames(temp_genome_counts) %in% rownames(post4))]
identical(sort(colnames(post4.counts)),sort(as.character(rownames(post4))))
post4_nmds <- sample_metadata %>%
filter(time_point == "1_Acclimation" | time_point == "6_Post-FMT2")4.3.2.11 Number of samples used
[1] 54
beta_div_richness_post4<-hillpair(data=post4.counts, q=0)
beta_div_neutral_post4<-hillpair(data=post4.counts, q=1)
beta_div_phylo_post4<-hillpair(data=post4.counts, q=1, tree=genome_tree)#Arrange of metadata dataframe
post4_arrange<-post4[labels(beta_div_neutral_post4$S),]
post4_arrange$type_time <- interaction(post4_arrange$type, post4_arrange$time_point)4.3.2.11.1 Richness
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.06809 0.034047 3.8471 999 0.03 *
Residuals 51 0.45135 0.008850
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.0300000 0.878
Hot_control 0.0349385 0.003
Treatment 0.8855174 0.0047257
adonis2(formula=beta_div_richness_post4$S ~ time_point*Population, data=post4[labels(beta_div_richness_post4$S),], permutations=999,strata=post4$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 3 | 3.310172 | 0.1883377 | 3.867324 | 0.001 |
| Residual | 50 | 14.265560 | 0.8116623 | NA | NA |
| Total | 53 | 17.575732 | 1.0000000 | NA | NA |
pairwise <- pairwise.adonis(beta_div_richness_post4$S, post4_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.3620815 | 1.052109 | 0.06169963 | 0.317 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.2800877 | 4.605444 | 0.22350616 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.8430295 | 2.845779 | 0.15100353 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.5232174 | 1.683240 | 0.09518843 | 0.020 | 0.300 | |
| Control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 1.1217138 | 3.634271 | 0.18509835 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.3606630 | 5.087152 | 0.24124415 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.9130048 | 3.195028 | 0.16645080 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.5959230 | 1.984036 | 0.11032208 | 0.002 | 0.030 | . |
| Treatment.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 1.2747787 | 4.275366 | 0.21086503 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.6397330 | 2.913695 | 0.15405213 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 1.4575447 | 6.224524 | 0.28007456 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.3276169 | 1.412318 | 0.08111028 | 0.041 | 0.615 | |
| Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 | 1 | 0.6463814 | 2.560441 | 0.13795154 | 0.001 | 0.015 | . |
| Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.4796256 | 1.916520 | 0.10696943 | 0.001 | 0.015 | . |
| Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 1.1305044 | 4.268317 | 0.21059061 | 0.001 | 0.015 | . |
4.3.2.11.2 Neutral
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.01544 0.0154447 2.0972 999 0.144
Residuals 52 0.38294 0.0073643
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
1_Acclimation 6_Post-FMT2
1_Acclimation 0.141
6_Post-FMT2 0.15357
adonis2(formula=beta_div_neutral_post4$S ~ time_point*Population, data=post4[labels(beta_div_neutral_post4$S),], permutations=999,strata=post4$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 3 | 3.863228 | 0.229321 | 4.959284 | 0.001 |
| Residual | 50 | 12.983151 | 0.770679 | NA | NA |
| Total | 53 | 16.846379 | 1.000000 | NA | NA |
pairwise <- pairwise.adonis(beta_div_neutral_post4$S, post4_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.2316020 | 0.7712905 | 0.04598874 | 0.716 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.4015347 | 5.7562378 | 0.26457873 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 1.1746426 | 4.5564741 | 0.22165640 | 0.001 | 0.015 | . |
| Control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.5286441 | 1.9819408 | 0.11021840 | 0.004 | 0.060 | |
| Control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 1.3443224 | 4.9104417 | 0.23483204 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.6347704 | 6.8326887 | 0.29925029 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 1.3540292 | 5.3398081 | 0.25022756 | 0.002 | 0.030 | . |
| Treatment.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.6311089 | 2.4041625 | 0.13063146 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 1.6125755 | 5.9825981 | 0.27215155 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.6202327 | 3.1519868 | 0.16457754 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 1.5701179 | 7.6327037 | 0.32297209 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.3634438 | 1.7083388 | 0.09647087 | 0.039 | 0.585 | |
| Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 | 1 | 1.0227481 | 4.6483346 | 0.22511910 | 0.001 | 0.015 | . |
| Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.5010202 | 2.2065321 | 0.12119453 | 0.001 | 0.015 | . |
| Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 1.3619424 | 5.7710313 | 0.26507845 | 0.001 | 0.015 | . |
4.3.2.11.3 Phylogenetic
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 1 0.06978 0.069777 5.0345 999 0.036 *
Residuals 52 0.72071 0.013860
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
1_Acclimation 6_Post-FMT2
1_Acclimation 0.041
6_Post-FMT2 0.029131
adonis2(formula=beta_div_phylo_post4$S ~ time_point*Population, data=post4[labels(beta_div_phylo_post4$S),], permutations=999,strata=post4$individual) %>%
as.matrix() %>%
kable()| Df | SumOfSqs | R2 | F | Pr(>F) | |
|---|---|---|---|---|---|
| Model | 3 | 0.757493 | 0.2376349 | 5.195124 | 0.001 |
| Residual | 50 | 2.430141 | 0.7623651 | NA | NA |
| Total | 53 | 3.187634 | 1.0000000 | NA | NA |
pairwise <- pairwise.adonis(beta_div_phylo_post4$S, post4_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.04186923 | 0.4391642 | 0.02671451 | 0.723 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.15609416 | 2.5546889 | 0.13768428 | 0.033 | 0.495 | |
| Control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.26322331 | 4.3060281 | 0.21205664 | 0.009 | 0.135 | |
| Control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.16047895 | 2.5405742 | 0.13702781 | 0.046 | 0.690 | |
| Control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.25529510 | 4.0109138 | 0.20043631 | 0.001 | 0.015 | . |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.23108846 | 4.0521838 | 0.20208192 | 0.002 | 0.030 | . |
| Treatment.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.36496892 | 6.3966666 | 0.28560797 | 0.003 | 0.045 | . |
| Treatment.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.22628210 | 3.8292220 | 0.19311005 | 0.021 | 0.315 | |
| Treatment.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.34830814 | 5.8463335 | 0.26761166 | 0.002 | 0.030 | . |
| Hot_control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.10002871 | 4.3836237 | 0.21505615 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.12577510 | 5.0601287 | 0.24027055 | 0.001 | 0.015 | . |
| Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.06334378 | 2.4997737 | 0.13512455 | 0.028 | 0.420 | |
| Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 | 1 | 0.05927454 | 2.3820253 | 0.12958449 | 0.030 | 0.450 | |
| Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.06906280 | 2.7224602 | 0.14541146 | 0.006 | 0.090 | |
| Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.11081709 | 4.0436561 | 0.20174244 | 0.004 | 0.060 |
beta_richness_nmds_post4 <- beta_div_richness_post4$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post4_nmds, by = c("sample" = "Tube_code"))
beta_neutral_nmds_post4 <- beta_div_neutral_post4$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post4_nmds, by = c("sample" = "Tube_code"))
beta_phylo_nmds_post4 <- beta_div_phylo_post4$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(post4_nmds, by = join_by(sample == Tube_code))4.3.2.12 All time comparison
all_comparison <- sample_metadata %>%
filter(time_point == "1_Acclimation" | time_point == "5_Post-FMT1"| time_point == "6_Post-FMT2"| time_point == "2_Antibiotics"| time_point == "3_Transplant1"| time_point == "4_Transplant2")%>%
mutate(Tube_code=str_remove_all(Tube_code, "_a"))
all_comparison$newID <- paste(all_comparison$Tube_code, "_", all_comparison$individual)
all_comparison_data<-all_comparison%>%
filter(Tube_code != "AD45_a") %>%
filter(Tube_code != "AD48_a") %>%
#mutate(newID=str_remove_all(newID, "_a")) %>%
column_to_rownames("newID")
all_comparison_nmds <- sample_metadata %>%
filter(time_point =="1_Acclimation" | time_point == "5_Post-FMT1"| time_point == "6_Post-FMT2"| time_point == "2_Antibiotics"| time_point == "3_Transplant1"| time_point == "4_Transplant2") %>%
filter(Tube_code != "AD45_a"| Tube_code != "AD48_a")
all_comparison_nmds$newID <- paste(all_comparison_nmds$Tube_code, "_", all_comparison_nmds$individual)
full_counts_new<-temp_genome_counts %>%
t()%>%
as.data.frame()%>%
rownames_to_column("Tube_code")%>%
full_join(all_comparison,by = join_by(Tube_code == Tube_code))
comparison_all_counts<-full_counts_new %>%
filter(time_point == "1_Acclimation" | time_point == "5_Post-FMT1"| time_point == "6_Post-FMT2"| time_point == "2_Antibiotics"| time_point == "3_Transplant1"| time_point == "4_Transplant2") %>%
subset(select=-c(315:324)) %>%
column_to_rownames("newID")%>%
subset(select=-c(1))%>%
t() %>%
as.data.frame() %>%
mutate_if(is.character, as.numeric) %>%
subset(select=-c(150:151))
identical(sort(colnames(comparison_all_counts)),sort(as.character(rownames(all_comparison))))beta_div_richness_all_comparison<-hillpair(data=comparison_all_counts, q=0)
beta_div_neutral_all_comparison<-hillpair(data=comparison_all_counts, q=1)
beta_div_phylo_all_comparison<-hillpair(data=comparison_all_counts, q=1, tree=genome_tree)#Arrange of metadata dataframe
all_comparison_arrange<-all_comparison_data[labels(beta_div_neutral_all_comparison$S),]
all_comparison_arrange$type_time <- interaction(all_comparison_arrange$type, all_comparison_arrange$time_point)4.3.2.12.1 Richness
betadisper(beta_div_richness_all_comparison$S, all_comparison_arrange$type) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.09854 0.049271 3.9422 999 0.022 *
Residuals 146 1.82474 0.012498
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.0390000 0.513
Hot_control 0.0359859 0.011
Treatment 0.5501317 0.0097545
#(formula=beta_div_richness_all_comparison$S ~ time_point*type, data=all_comparison_data[labels(beta_div_richness_all_comparison$S),], permutations=999,strata=all_comparison_data$individual) %>%
#as.matrix() %>%
#kable()pairwise <- pairwise.adonis(beta_div_richness_all_comparison$S, all_comparison_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.362081458 | 1.05210877 | 0.061699628 | 0.320 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.280087737 | 4.60544357 | 0.223506160 | 0.002 | 0.306 | |
| Control.1_Acclimation vs Control.2_Antibiotics | 1 | 1.011407380 | 2.82990350 | 0.158716703 | 0.001 | 0.153 | |
| Control.1_Acclimation vs Treatment.2_Antibiotics | 1 | 0.920434738 | 2.46321443 | 0.149619289 | 0.006 | 0.918 | |
| Control.1_Acclimation vs Hot_control.2_Antibiotics | 1 | 1.373846937 | 4.00511266 | 0.210738696 | 0.001 | 0.153 | |
| Control.1_Acclimation vs Control.3_Transplant1 | 1 | 0.488555082 | 1.74347500 | 0.098260065 | 0.012 | 1.000 | |
| Control.1_Acclimation vs Treatment.3_Transplant1 | 1 | 1.211627129 | 4.33073710 | 0.236255481 | 0.001 | 0.153 | |
| Control.1_Acclimation vs Hot_control.3_Transplant1 | 1 | 1.314649861 | 4.86501559 | 0.244903688 | 0.001 | 0.153 | |
| Control.1_Acclimation vs Control.4_Transplant2 | 1 | 0.402848480 | 1.44978255 | 0.083083130 | 0.087 | 1.000 | |
| Control.1_Acclimation vs Treatment.4_Transplant2 | 1 | 1.132799430 | 3.99953382 | 0.235273147 | 0.002 | 0.306 | |
| Control.1_Acclimation vs Hot_control.4_Transplant2 | 1 | 1.239570207 | 4.51495838 | 0.243854633 | 0.002 | 0.306 | |
| Control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.684565686 | 1.99811396 | 0.111017964 | 0.004 | 0.612 | |
| Control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.843746089 | 2.49923230 | 0.142819539 | 0.001 | 0.153 | |
| Control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 1.120802205 | 3.56866951 | 0.182366487 | 0.001 | 0.153 | |
| Control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.843029506 | 2.84577913 | 0.151003527 | 0.002 | 0.306 | |
| Control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.523217385 | 1.68323982 | 0.095188429 | 0.029 | 1.000 | |
| Control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 1.121713759 | 3.63427125 | 0.185098352 | 0.001 | 0.153 | |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.360662985 | 5.08715202 | 0.241244148 | 0.001 | 0.153 | |
| Treatment.1_Acclimation vs Control.2_Antibiotics | 1 | 0.970703875 | 2.80371407 | 0.157479167 | 0.001 | 0.153 | |
| Treatment.1_Acclimation vs Treatment.2_Antibiotics | 1 | 0.827773826 | 2.28859986 | 0.140503167 | 0.005 | 0.765 | |
| Treatment.1_Acclimation vs Hot_control.2_Antibiotics | 1 | 1.455154159 | 4.38505425 | 0.226207995 | 0.001 | 0.153 | |
| Treatment.1_Acclimation vs Control.3_Transplant1 | 1 | 0.481272253 | 1.78421716 | 0.100325876 | 0.008 | 1.000 | |
| Treatment.1_Acclimation vs Treatment.3_Transplant1 | 1 | 1.312567757 | 4.90136959 | 0.259312933 | 0.001 | 0.153 | |
| Treatment.1_Acclimation vs Hot_control.3_Transplant1 | 1 | 1.417501836 | 5.47200891 | 0.267292230 | 0.001 | 0.153 | |
| Treatment.1_Acclimation vs Control.4_Transplant2 | 1 | 0.377460322 | 1.41165944 | 0.081075525 | 0.080 | 1.000 | |
| Treatment.1_Acclimation vs Treatment.4_Transplant2 | 1 | 1.238098167 | 4.57988771 | 0.260518599 | 0.001 | 0.153 | |
| Treatment.1_Acclimation vs Hot_control.4_Transplant2 | 1 | 1.348911044 | 5.13734533 | 0.268446080 | 0.001 | 0.153 | |
| Treatment.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.721620019 | 2.17273370 | 0.119560092 | 0.001 | 0.153 | |
| Treatment.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.955130770 | 2.92605353 | 0.163229097 | 0.001 | 0.153 | |
| Treatment.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 1.226334542 | 4.03948698 | 0.201576367 | 0.001 | 0.153 | |
| Treatment.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.913004785 | 3.19502772 | 0.166450800 | 0.002 | 0.306 | |
| Treatment.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.595923023 | 1.98403628 | 0.110322079 | 0.001 | 0.153 | |
| Treatment.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 1.274778685 | 4.27536566 | 0.210865033 | 0.001 | 0.153 | |
| Hot_control.1_Acclimation vs Control.2_Antibiotics | 1 | 1.734607158 | 6.29369579 | 0.295566155 | 0.001 | 0.153 | |
| Hot_control.1_Acclimation vs Treatment.2_Antibiotics | 1 | 1.563478726 | 5.46593706 | 0.280794962 | 0.001 | 0.153 | |
| Hot_control.1_Acclimation vs Hot_control.2_Antibiotics | 1 | 1.232843872 | 4.71931579 | 0.239324520 | 0.001 | 0.153 | |
| Hot_control.1_Acclimation vs Control.3_Transplant1 | 1 | 1.296297619 | 6.36871987 | 0.284715438 | 0.001 | 0.153 | |
| Hot_control.1_Acclimation vs Treatment.3_Transplant1 | 1 | 0.220244257 | 1.14625733 | 0.075679246 | 0.267 | 1.000 | |
| Hot_control.1_Acclimation vs Hot_control.3_Transplant1 | 1 | 0.224204694 | 1.18982182 | 0.073491965 | 0.256 | 1.000 | |
| Hot_control.1_Acclimation vs Control.4_Transplant2 | 1 | 1.399239883 | 6.95480534 | 0.302978189 | 0.001 | 0.153 | |
| Hot_control.1_Acclimation vs Treatment.4_Transplant2 | 1 | 0.216302837 | 1.14530694 | 0.080967274 | 0.306 | 1.000 | |
| Hot_control.1_Acclimation vs Hot_control.4_Transplant2 | 1 | 0.196664515 | 1.05215668 | 0.069900726 | 0.376 | 1.000 | |
| Hot_control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 1.431979200 | 5.38483613 | 0.251806284 | 0.001 | 0.153 | |
| Hot_control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.817241324 | 3.19468982 | 0.175583637 | 0.001 | 0.153 | |
| Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.579613496 | 2.44161478 | 0.132397016 | 0.001 | 0.153 | |
| Hot_control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.639732962 | 2.91369490 | 0.154052125 | 0.001 | 0.153 | |
| Hot_control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 1.457544655 | 6.22452356 | 0.280074556 | 0.001 | 0.153 | |
| Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.327616900 | 1.41231806 | 0.081110284 | 0.034 | 1.000 | |
| Control.2_Antibiotics vs Treatment.2_Antibiotics | 1 | 0.300761563 | 0.79496206 | 0.057626984 | 0.802 | 1.000 | |
| Control.2_Antibiotics vs Hot_control.2_Antibiotics | 1 | 1.097851301 | 3.18067161 | 0.185130808 | 0.002 | 0.306 | |
| Control.2_Antibiotics vs Control.3_Transplant1 | 1 | 1.540776008 | 5.54176964 | 0.269780537 | 0.001 | 0.153 | |
| Control.2_Antibiotics vs Treatment.3_Transplant1 | 1 | 1.711923036 | 6.17547381 | 0.322050650 | 0.001 | 0.153 | |
| Control.2_Antibiotics vs Hot_control.3_Transplant1 | 1 | 1.841780800 | 6.89378453 | 0.329944272 | 0.001 | 0.153 | |
| Control.2_Antibiotics vs Control.4_Transplant2 | 1 | 1.511441382 | 5.48573893 | 0.267783308 | 0.001 | 0.153 | |
| Control.2_Antibiotics vs Treatment.4_Transplant2 | 1 | 1.612594527 | 5.74394061 | 0.323712795 | 0.001 | 0.153 | |
| Control.2_Antibiotics vs Hot_control.4_Transplant2 | 1 | 1.739395066 | 6.40461266 | 0.330056197 | 0.001 | 0.153 | |
| Control.2_Antibiotics vs Control.5_Post-FMT1 | 1 | 0.971238688 | 2.81865279 | 0.158185516 | 0.001 | 0.153 | |
| Control.2_Antibiotics vs Treatment.5_Post-FMT1 | 1 | 1.190641768 | 3.50854402 | 0.200390393 | 0.001 | 0.153 | |
| Control.2_Antibiotics vs Hot_control.5_Post-FMT1 | 1 | 1.551250242 | 4.93817626 | 0.247674421 | 0.001 | 0.153 | |
| Control.2_Antibiotics vs Treatment.6_Post-FMT2 | 1 | 1.490970612 | 5.05213442 | 0.251949958 | 0.001 | 0.153 | |
| Control.2_Antibiotics vs Control.6_Post-FMT2 | 1 | 1.198436711 | 3.85732325 | 0.204553064 | 0.001 | 0.153 | |
| Control.2_Antibiotics vs Hot_control.6_Post-FMT2 | 1 | 1.546155402 | 5.01421139 | 0.250532549 | 0.001 | 0.153 | |
| Treatment.2_Antibiotics vs Hot_control.2_Antibiotics | 1 | 1.069552526 | 2.95663926 | 0.185292104 | 0.001 | 0.153 | |
| Treatment.2_Antibiotics vs Control.3_Transplant1 | 1 | 1.332989733 | 4.61829348 | 0.248051385 | 0.001 | 0.153 | |
| Treatment.2_Antibiotics vs Treatment.3_Transplant1 | 1 | 1.516514105 | 5.23811421 | 0.303868169 | 0.001 | 0.153 | |
| Treatment.2_Antibiotics vs Hot_control.3_Transplant1 | 1 | 1.623847037 | 5.84647092 | 0.310215687 | 0.001 | 0.153 | |
| Treatment.2_Antibiotics vs Control.4_Transplant2 | 1 | 1.316537672 | 4.60415208 | 0.247479813 | 0.001 | 0.153 | |
| Treatment.2_Antibiotics vs Treatment.4_Transplant2 | 1 | 1.438049740 | 4.88320766 | 0.307444678 | 0.001 | 0.153 | |
| Treatment.2_Antibiotics vs Hot_control.4_Transplant2 | 1 | 1.548346381 | 5.46312423 | 0.312837735 | 0.003 | 0.459 | |
| Treatment.2_Antibiotics vs Control.5_Post-FMT1 | 1 | 0.802328938 | 2.22911438 | 0.137352805 | 0.004 | 0.612 | |
| Treatment.2_Antibiotics vs Treatment.5_Post-FMT1 | 1 | 0.974454304 | 2.74115182 | 0.174139215 | 0.005 | 0.765 | |
| Treatment.2_Antibiotics vs Hot_control.5_Post-FMT1 | 1 | 1.304972399 | 3.98688766 | 0.221655227 | 0.001 | 0.153 | |
| Treatment.2_Antibiotics vs Treatment.6_Post-FMT2 | 1 | 1.278825644 | 4.16636433 | 0.229344973 | 0.001 | 0.153 | |
| Treatment.2_Antibiotics vs Control.6_Post-FMT2 | 1 | 1.017603829 | 3.14437063 | 0.183405428 | 0.001 | 0.153 | |
| Treatment.2_Antibiotics vs Hot_control.6_Post-FMT2 | 1 | 1.369140221 | 4.26359194 | 0.233447613 | 0.001 | 0.153 | |
| Hot_control.2_Antibiotics vs Control.3_Transplant1 | 1 | 1.825343641 | 6.92328189 | 0.315795871 | 0.001 | 0.153 | |
| Hot_control.2_Antibiotics vs Treatment.3_Transplant1 | 1 | 1.275008094 | 4.89212161 | 0.273423226 | 0.001 | 0.153 | |
| Hot_control.2_Antibiotics vs Hot_control.3_Transplant1 | 1 | 1.365719305 | 5.42464757 | 0.279266203 | 0.001 | 0.153 | |
| Hot_control.2_Antibiotics vs Control.4_Transplant2 | 1 | 1.871048444 | 7.16478012 | 0.323250674 | 0.001 | 0.153 | |
| Hot_control.2_Antibiotics vs Treatment.4_Transplant2 | 1 | 1.264776297 | 4.81313017 | 0.286272105 | 0.001 | 0.153 | |
| Hot_control.2_Antibiotics vs Hot_control.4_Transplant2 | 1 | 1.336062206 | 5.23953594 | 0.287262569 | 0.002 | 0.306 | |
| Hot_control.2_Antibiotics vs Control.5_Post-FMT1 | 1 | 1.256421540 | 3.80504531 | 0.202341725 | 0.001 | 0.153 | |
| Hot_control.2_Antibiotics vs Treatment.5_Post-FMT1 | 1 | 1.042883208 | 3.21925840 | 0.186956855 | 0.001 | 0.153 | |
| Hot_control.2_Antibiotics vs Hot_control.5_Post-FMT1 | 1 | 1.118057885 | 3.72987362 | 0.199140352 | 0.001 | 0.153 | |
| Hot_control.2_Antibiotics vs Treatment.6_Post-FMT2 | 1 | 1.393703594 | 4.96438715 | 0.248662136 | 0.001 | 0.153 | |
| Hot_control.2_Antibiotics vs Control.6_Post-FMT2 | 1 | 1.578393989 | 5.32675023 | 0.262056166 | 0.001 | 0.153 | |
| Hot_control.2_Antibiotics vs Hot_control.6_Post-FMT2 | 1 | 1.187086833 | 4.03801261 | 0.212102633 | 0.001 | 0.153 | |
| Control.3_Transplant1 vs Treatment.3_Transplant1 | 1 | 1.129362662 | 5.79950801 | 0.292911723 | 0.001 | 0.153 | |
| Control.3_Transplant1 vs Hot_control.3_Transplant1 | 1 | 1.208865861 | 6.33395382 | 0.296895450 | 0.001 | 0.153 | |
| Control.3_Transplant1 vs Control.4_Transplant2 | 1 | 0.131647583 | 0.64704882 | 0.038868680 | 0.864 | 1.000 | |
| Control.3_Transplant1 vs Treatment.4_Transplant2 | 1 | 1.120646022 | 5.84730567 | 0.310246237 | 0.001 | 0.153 | |
| Control.3_Transplant1 vs Hot_control.4_Transplant2 | 1 | 1.223919777 | 6.45841493 | 0.315685010 | 0.001 | 0.153 | |
| Control.3_Transplant1 vs Control.5_Post-FMT1 | 1 | 0.859252354 | 3.20381975 | 0.166832422 | 0.001 | 0.153 | |
| Control.3_Transplant1 vs Treatment.5_Post-FMT1 | 1 | 1.027552599 | 3.97918760 | 0.209660586 | 0.001 | 0.153 | |
| Control.3_Transplant1 vs Hot_control.5_Post-FMT1 | 1 | 1.201969568 | 5.01536301 | 0.238652219 | 0.001 | 0.153 | |
| Control.3_Transplant1 vs Treatment.6_Post-FMT2 | 1 | 0.813822425 | 3.66869509 | 0.186524580 | 0.001 | 0.153 | |
| Control.3_Transplant1 vs Control.6_Post-FMT2 | 1 | 0.635206288 | 2.68665949 | 0.143774198 | 0.001 | 0.153 | |
| Control.3_Transplant1 vs Hot_control.6_Post-FMT2 | 1 | 1.251952139 | 5.34475979 | 0.250401496 | 0.001 | 0.153 | |
| Treatment.3_Transplant1 vs Hot_control.3_Transplant1 | 1 | 0.009102921 | 0.05153754 | 0.003948772 | 1.000 | 1.000 | |
| Treatment.3_Transplant1 vs Control.4_Transplant2 | 1 | 1.314300451 | 6.84362433 | 0.328331782 | 0.001 | 0.153 | |
| Treatment.3_Transplant1 vs Treatment.4_Transplant2 | 1 | 0.074927876 | 0.42820379 | 0.037469037 | 0.974 | 1.000 | |
| Treatment.3_Transplant1 vs Hot_control.4_Transplant2 | 1 | 0.077903563 | 0.44805710 | 0.035994139 | 0.963 | 1.000 | |
| Treatment.3_Transplant1 vs Control.5_Post-FMT1 | 1 | 1.353702886 | 5.08846981 | 0.266572955 | 0.001 | 0.153 | |
| Treatment.3_Transplant1 vs Treatment.5_Post-FMT1 | 1 | 0.791197505 | 3.11042526 | 0.193069097 | 0.002 | 0.306 | |
| Treatment.3_Transplant1 vs Hot_control.5_Post-FMT1 | 1 | 0.536176861 | 2.29706971 | 0.140949861 | 0.006 | 0.918 | |
| Treatment.3_Transplant1 vs Treatment.6_Post-FMT2 | 1 | 0.621029781 | 2.91505641 | 0.172335010 | 0.001 | 0.153 | |
| Treatment.3_Transplant1 vs Control.6_Post-FMT2 | 1 | 1.331819749 | 5.79735453 | 0.292834809 | 0.001 | 0.153 | |
| Treatment.3_Transplant1 vs Hot_control.6_Post-FMT2 | 1 | 0.417936103 | 1.83930234 | 0.116122686 | 0.005 | 0.765 | |
| Hot_control.3_Transplant1 vs Control.4_Transplant2 | 1 | 1.410186386 | 7.48716456 | 0.332952807 | 0.001 | 0.153 | |
| Hot_control.3_Transplant1 vs Treatment.4_Transplant2 | 1 | 0.077823551 | 0.45304555 | 0.036380301 | 0.958 | 1.000 | |
| Hot_control.3_Transplant1 vs Hot_control.4_Transplant2 | 1 | 0.067817527 | 0.39659721 | 0.029604324 | 0.985 | 1.000 | |
| Hot_control.3_Transplant1 vs Control.5_Post-FMT1 | 1 | 1.469262870 | 5.70807523 | 0.275644895 | 0.001 | 0.153 | |
| Hot_control.3_Transplant1 vs Treatment.5_Post-FMT1 | 1 | 0.865197810 | 3.51773013 | 0.200809700 | 0.001 | 0.153 | |
| Hot_control.3_Transplant1 vs Hot_control.5_Post-FMT1 | 1 | 0.610686942 | 2.69073097 | 0.152098349 | 0.001 | 0.153 | |
| Hot_control.3_Transplant1 vs Treatment.6_Post-FMT2 | 1 | 0.678638979 | 3.26359386 | 0.178693957 | 0.001 | 0.153 | |
| Hot_control.3_Transplant1 vs Control.6_Post-FMT2 | 1 | 1.439165056 | 6.43874075 | 0.300332040 | 0.001 | 0.153 | |
| Hot_control.3_Transplant1 vs Hot_control.6_Post-FMT2 | 1 | 0.458703763 | 2.07389437 | 0.121465808 | 0.002 | 0.306 | |
| Control.4_Transplant2 vs Treatment.4_Transplant2 | 1 | 1.248404869 | 6.61377735 | 0.337200593 | 0.001 | 0.153 | |
| Control.4_Transplant2 vs Hot_control.4_Transplant2 | 1 | 1.357472348 | 7.26616675 | 0.341677315 | 0.001 | 0.153 | |
| Control.4_Transplant2 vs Control.5_Post-FMT1 | 1 | 0.833650830 | 3.13584966 | 0.163873030 | 0.001 | 0.153 | |
| Control.4_Transplant2 vs Treatment.5_Post-FMT1 | 1 | 1.080414607 | 4.22492316 | 0.219762811 | 0.001 | 0.153 | |
| Control.4_Transplant2 vs Hot_control.5_Post-FMT1 | 1 | 1.341135755 | 5.65148950 | 0.261020818 | 0.001 | 0.153 | |
| Control.4_Transplant2 vs Treatment.6_Post-FMT2 | 1 | 0.926258626 | 4.22028175 | 0.208715279 | 0.001 | 0.153 | |
| Control.4_Transplant2 vs Control.6_Post-FMT2 | 1 | 0.574654578 | 2.45496178 | 0.133024484 | 0.001 | 0.153 | |
| Control.4_Transplant2 vs Hot_control.6_Post-FMT2 | 1 | 1.352206080 | 5.83128366 | 0.267106770 | 0.001 | 0.153 | |
| Treatment.4_Transplant2 vs Hot_control.4_Transplant2 | 1 | 0.012433593 | 0.07386441 | 0.006670157 | 1.000 | 1.000 | |
| Treatment.4_Transplant2 vs Control.5_Post-FMT1 | 1 | 1.291262283 | 4.81032755 | 0.270086417 | 0.001 | 0.153 | |
| Treatment.4_Transplant2 vs Treatment.5_Post-FMT1 | 1 | 0.771548746 | 3.01386503 | 0.200738786 | 0.002 | 0.306 | |
| Treatment.4_Transplant2 vs Hot_control.5_Post-FMT1 | 1 | 0.598950488 | 2.56717762 | 0.164909637 | 0.002 | 0.306 | |
| Treatment.4_Transplant2 vs Treatment.6_Post-FMT2 | 1 | 0.584788938 | 2.76668773 | 0.175476788 | 0.001 | 0.153 | |
| Treatment.4_Transplant2 vs Control.6_Post-FMT2 | 1 | 1.258012524 | 5.48540220 | 0.296742378 | 0.001 | 0.153 | |
| Treatment.4_Transplant2 vs Hot_control.6_Post-FMT2 | 1 | 0.432278420 | 1.90731713 | 0.127945029 | 0.002 | 0.306 | |
| Hot_control.4_Transplant2 vs Control.5_Post-FMT1 | 1 | 1.399019323 | 5.36419518 | 0.277016170 | 0.001 | 0.153 | |
| Hot_control.4_Transplant2 vs Treatment.5_Post-FMT1 | 1 | 0.829245656 | 3.33377055 | 0.204102938 | 0.001 | 0.153 | |
| Hot_control.4_Transplant2 vs Hot_control.5_Post-FMT1 | 1 | 0.652357925 | 2.85882027 | 0.169574159 | 0.001 | 0.153 | |
| Hot_control.4_Transplant2 vs Treatment.6_Post-FMT2 | 1 | 0.631349786 | 3.03802796 | 0.178308661 | 0.001 | 0.153 | |
| Hot_control.4_Transplant2 vs Control.6_Post-FMT2 | 1 | 1.377318091 | 6.13498096 | 0.304692662 | 0.001 | 0.153 | |
| Hot_control.4_Transplant2 vs Hot_control.6_Post-FMT2 | 1 | 0.447315329 | 2.01494401 | 0.125816488 | 0.002 | 0.306 | |
| Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 | 1 | 0.561541773 | 1.72900374 | 0.103353658 | 0.021 | 1.000 | |
| Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.843842909 | 2.79377183 | 0.148654132 | 0.001 | 0.153 | |
| Control.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.762813539 | 2.68392536 | 0.143648902 | 0.003 | 0.459 | |
| Control.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 0.343260479 | 1.14873316 | 0.066986474 | 0.257 | 1.000 | |
| Control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 1.126958011 | 3.79925624 | 0.191888836 | 0.001 | 0.153 | |
| Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.373492058 | 1.26892925 | 0.077997097 | 0.114 | 1.000 | |
| Treatment.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.357139663 | 1.29718436 | 0.079595612 | 0.122 | 1.000 | |
| Treatment.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 0.776946658 | 2.67089786 | 0.151146698 | 0.001 | 0.153 | |
| Treatment.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 0.650235954 | 2.25340711 | 0.130606500 | 0.001 | 0.153 | |
| Hot_control.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.413209131 | 1.61613759 | 0.091741881 | 0.013 | 1.000 | |
| Hot_control.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 1.016399223 | 3.76057141 | 0.190306815 | 0.001 | 0.153 | |
| Hot_control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 0.273256319 | 1.01928120 | 0.059889791 | 0.435 | 1.000 | |
| Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 | 1 | 0.646381353 | 2.56044149 | 0.137951540 | 0.001 | 0.153 | |
| Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.479625583 | 1.91651999 | 0.106969433 | 0.001 | 0.153 | |
| Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 1.130504372 | 4.26831718 | 0.210590605 | 0.001 | 0.153 |
4.3.2.12.2 Neutral
betadisper(beta_div_neutral_all_comparison$S, all_comparison_arrange$type) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 2 0.11629 0.058145 4.2503 999 0.022 *
Residuals 146 1.99732 0.013680
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Control Hot_control Treatment
Control 0.1050000 0.213
Hot_control 0.0992801 0.005
Treatment 0.1988089 0.0039096
#adonis2(formula=beta_div_neutral_all_comparison$S ~ time_point*type, data=all_comparison_arrange[labels(beta_div_neutral_all_comparison$S),], permutations=999,strata=all_comparison_arrange$individual) %>%
#as.matrix() %>%
#kable()pairwise <- pairwise.adonis(beta_div_neutral_all_comparison$S, all_comparison_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.231601965 | 0.77129054 | 0.045988741 | 0.726 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.401534736 | 5.75623784 | 0.264578733 | 0.001 | 0.153 | |
| Control.1_Acclimation vs Control.2_Antibiotics | 1 | 1.052408777 | 3.24991568 | 0.178078394 | 0.001 | 0.153 | |
| Control.1_Acclimation vs Treatment.2_Antibiotics | 1 | 1.111553177 | 3.41189169 | 0.195951810 | 0.002 | 0.306 | |
| Control.1_Acclimation vs Hot_control.2_Antibiotics | 1 | 1.765907003 | 6.07675876 | 0.288315619 | 0.001 | 0.153 | |
| Control.1_Acclimation vs Control.3_Transplant1 | 1 | 0.537039622 | 2.17218034 | 0.119533281 | 0.004 | 0.612 | |
| Control.1_Acclimation vs Treatment.3_Transplant1 | 1 | 1.405564870 | 5.47754050 | 0.281223417 | 0.001 | 0.153 | |
| Control.1_Acclimation vs Hot_control.3_Transplant1 | 1 | 1.533003108 | 6.15337104 | 0.290893164 | 0.001 | 0.153 | |
| Control.1_Acclimation vs Control.4_Transplant2 | 1 | 0.352392363 | 1.45768829 | 0.083498357 | 0.084 | 1.000 | |
| Control.1_Acclimation vs Treatment.4_Transplant2 | 1 | 1.310880994 | 5.15468039 | 0.283931211 | 0.001 | 0.153 | |
| Control.1_Acclimation vs Hot_control.4_Transplant2 | 1 | 1.444103332 | 5.83290868 | 0.294102533 | 0.001 | 0.153 | |
| Control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.833216183 | 2.90811026 | 0.153802269 | 0.001 | 0.153 | |
| Control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 1.171959521 | 4.06855143 | 0.213364473 | 0.001 | 0.153 | |
| Control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 1.426087479 | 5.24131714 | 0.246751042 | 0.001 | 0.153 | |
| Control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 1.174642637 | 4.55647410 | 0.221656402 | 0.001 | 0.153 | |
| Control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.528644071 | 1.98194080 | 0.110218403 | 0.002 | 0.306 | |
| Control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 1.344322430 | 4.91044173 | 0.234832042 | 0.001 | 0.153 | |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 1.634770392 | 6.83268874 | 0.299250291 | 0.001 | 0.153 | |
| Treatment.1_Acclimation vs Control.2_Antibiotics | 1 | 1.025810137 | 3.21247184 | 0.176388569 | 0.001 | 0.153 | |
| Treatment.1_Acclimation vs Treatment.2_Antibiotics | 1 | 0.881227194 | 2.74559155 | 0.163959066 | 0.004 | 0.612 | |
| Treatment.1_Acclimation vs Hot_control.2_Antibiotics | 1 | 1.875954242 | 6.55710727 | 0.304173802 | 0.001 | 0.153 | |
| Treatment.1_Acclimation vs Control.3_Transplant1 | 1 | 0.566432465 | 2.33088738 | 0.127156276 | 0.003 | 0.459 | |
| Treatment.1_Acclimation vs Treatment.3_Transplant1 | 1 | 1.572048670 | 6.24379035 | 0.308429906 | 0.001 | 0.153 | |
| Treatment.1_Acclimation vs Hot_control.3_Transplant1 | 1 | 1.701844881 | 6.95690050 | 0.316843468 | 0.001 | 0.153 | |
| Treatment.1_Acclimation vs Control.4_Transplant2 | 1 | 0.401361323 | 1.68977427 | 0.095522659 | 0.044 | 1.000 | |
| Treatment.1_Acclimation vs Treatment.4_Transplant2 | 1 | 1.459328039 | 5.85815805 | 0.310643173 | 0.001 | 0.153 | |
| Treatment.1_Acclimation vs Hot_control.4_Transplant2 | 1 | 1.598342225 | 6.58427533 | 0.319869183 | 0.001 | 0.153 | |
| Treatment.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.951763439 | 3.37156998 | 0.174047327 | 0.001 | 0.153 | |
| Treatment.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 1.312777350 | 4.62982556 | 0.235856684 | 0.001 | 0.153 | |
| Treatment.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 1.671336875 | 6.23954604 | 0.280560854 | 0.001 | 0.153 | |
| Treatment.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 1.354029183 | 5.33980806 | 0.250227558 | 0.001 | 0.153 | |
| Treatment.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.631108857 | 2.40416255 | 0.130631456 | 0.001 | 0.153 | |
| Treatment.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 1.612575528 | 5.98259806 | 0.272151547 | 0.001 | 0.153 | |
| Hot_control.1_Acclimation vs Control.2_Antibiotics | 1 | 1.801830169 | 6.96394029 | 0.317062430 | 0.001 | 0.153 | |
| Hot_control.1_Acclimation vs Treatment.2_Antibiotics | 1 | 1.796058823 | 7.01450160 | 0.333793384 | 0.001 | 0.153 | |
| Hot_control.1_Acclimation vs Hot_control.2_Antibiotics | 1 | 1.140094120 | 5.05560699 | 0.252079481 | 0.001 | 0.153 | |
| Hot_control.1_Acclimation vs Control.3_Transplant1 | 1 | 1.409800825 | 7.57086193 | 0.321195803 | 0.001 | 0.153 | |
| Hot_control.1_Acclimation vs Treatment.3_Transplant1 | 1 | 0.268029239 | 1.43433504 | 0.092931444 | 0.137 | 1.000 | |
| Hot_control.1_Acclimation vs Hot_control.3_Transplant1 | 1 | 0.271118909 | 1.47312872 | 0.089426165 | 0.100 | 1.000 | |
| Hot_control.1_Acclimation vs Control.4_Transplant2 | 1 | 1.448236257 | 8.01342873 | 0.333706145 | 0.001 | 0.153 | |
| Hot_control.1_Acclimation vs Treatment.4_Transplant2 | 1 | 0.259223626 | 1.44651346 | 0.100128897 | 0.146 | 1.000 | |
| Hot_control.1_Acclimation vs Hot_control.4_Transplant2 | 1 | 0.220654933 | 1.24074949 | 0.081410005 | 0.262 | 1.000 | |
| Hot_control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 1.540978110 | 6.83380557 | 0.299284565 | 0.001 | 0.153 | |
| Hot_control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.913361414 | 4.09645344 | 0.214513834 | 0.001 | 0.153 | |
| Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.695483464 | 3.29512337 | 0.170774932 | 0.001 | 0.153 | |
| Hot_control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.620232728 | 3.15198682 | 0.164577537 | 0.001 | 0.153 | |
| Hot_control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 1.570117896 | 7.63270374 | 0.322972091 | 0.001 | 0.153 | |
| Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.363443763 | 1.70833881 | 0.096470867 | 0.038 | 1.000 | |
| Control.2_Antibiotics vs Treatment.2_Antibiotics | 1 | 0.309091831 | 0.88382033 | 0.063658295 | 0.586 | 1.000 | |
| Control.2_Antibiotics vs Hot_control.2_Antibiotics | 1 | 1.318315639 | 4.24836003 | 0.232807771 | 0.001 | 0.153 | |
| Control.2_Antibiotics vs Control.3_Transplant1 | 1 | 1.689711387 | 6.43107429 | 0.300081751 | 0.002 | 0.306 | |
| Control.2_Antibiotics vs Treatment.3_Transplant1 | 1 | 1.689195930 | 6.13766780 | 0.320711377 | 0.002 | 0.306 | |
| Control.2_Antibiotics vs Hot_control.3_Transplant1 | 1 | 1.809255022 | 6.80473556 | 0.327076282 | 0.001 | 0.153 | |
| Control.2_Antibiotics vs Control.4_Transplant2 | 1 | 1.677507510 | 6.53011334 | 0.303301392 | 0.001 | 0.153 | |
| Control.2_Antibiotics vs Treatment.4_Transplant2 | 1 | 1.627593397 | 5.93402835 | 0.330880951 | 0.001 | 0.153 | |
| Control.2_Antibiotics vs Hot_control.4_Transplant2 | 1 | 1.753895650 | 6.60608411 | 0.336940517 | 0.003 | 0.459 | |
| Control.2_Antibiotics vs Control.5_Post-FMT1 | 1 | 1.193795915 | 3.91871436 | 0.207134284 | 0.001 | 0.153 | |
| Control.2_Antibiotics vs Treatment.5_Post-FMT1 | 1 | 1.408395455 | 4.57891410 | 0.246457574 | 0.003 | 0.459 | |
| Control.2_Antibiotics vs Hot_control.5_Post-FMT1 | 1 | 1.733940673 | 5.99463357 | 0.285531707 | 0.001 | 0.153 | |
| Control.2_Antibiotics vs Treatment.6_Post-FMT2 | 1 | 1.520214567 | 5.54808874 | 0.270005099 | 0.001 | 0.153 | |
| Control.2_Antibiotics vs Control.6_Post-FMT2 | 1 | 1.242673544 | 4.38276233 | 0.226116498 | 0.001 | 0.153 | |
| Control.2_Antibiotics vs Hot_control.6_Post-FMT2 | 1 | 1.690913277 | 5.80983211 | 0.279186880 | 0.001 | 0.153 | |
| Treatment.2_Antibiotics vs Hot_control.2_Antibiotics | 1 | 1.496671964 | 4.80650925 | 0.269929899 | 0.003 | 0.459 | |
| Treatment.2_Antibiotics vs Control.3_Transplant1 | 1 | 1.585917342 | 6.09171700 | 0.303195441 | 0.001 | 0.153 | |
| Treatment.2_Antibiotics vs Treatment.3_Transplant1 | 1 | 1.638410357 | 5.99150564 | 0.333018579 | 0.001 | 0.153 | |
| Treatment.2_Antibiotics vs Hot_control.3_Transplant1 | 1 | 1.752495870 | 6.64990939 | 0.338419341 | 0.001 | 0.153 | |
| Treatment.2_Antibiotics vs Control.4_Transplant2 | 1 | 1.594532431 | 6.27600416 | 0.309528648 | 0.001 | 0.153 | |
| Treatment.2_Antibiotics vs Treatment.4_Transplant2 | 1 | 1.569536480 | 5.76455490 | 0.343853740 | 0.001 | 0.153 | |
| Treatment.2_Antibiotics vs Hot_control.4_Transplant2 | 1 | 1.692716606 | 6.43803058 | 0.349171271 | 0.001 | 0.153 | |
| Treatment.2_Antibiotics vs Control.5_Post-FMT1 | 1 | 1.203693742 | 3.94355452 | 0.219775548 | 0.001 | 0.153 | |
| Treatment.2_Antibiotics vs Treatment.5_Post-FMT1 | 1 | 1.342345723 | 4.35196799 | 0.250805441 | 0.001 | 0.153 | |
| Treatment.2_Antibiotics vs Hot_control.5_Post-FMT1 | 1 | 1.646821658 | 5.70346727 | 0.289465158 | 0.001 | 0.153 | |
| Treatment.2_Antibiotics vs Treatment.6_Post-FMT2 | 1 | 1.526094538 | 5.60220112 | 0.285794492 | 0.002 | 0.306 | |
| Treatment.2_Antibiotics vs Control.6_Post-FMT2 | 1 | 1.228569941 | 4.34707035 | 0.236935394 | 0.001 | 0.153 | |
| Treatment.2_Antibiotics vs Hot_control.6_Post-FMT2 | 1 | 1.612642625 | 5.54814849 | 0.283819641 | 0.001 | 0.153 | |
| Hot_control.2_Antibiotics vs Control.3_Transplant1 | 1 | 2.115607623 | 9.21771686 | 0.380618739 | 0.001 | 0.153 | |
| Hot_control.2_Antibiotics vs Treatment.3_Transplant1 | 1 | 1.101992427 | 4.65211649 | 0.263544403 | 0.002 | 0.306 | |
| Hot_control.2_Antibiotics vs Hot_control.3_Transplant1 | 1 | 1.133256122 | 4.92116448 | 0.260087823 | 0.003 | 0.459 | |
| Hot_control.2_Antibiotics vs Control.4_Transplant2 | 1 | 2.176300247 | 9.73032474 | 0.393457217 | 0.001 | 0.153 | |
| Hot_control.2_Antibiotics vs Treatment.4_Transplant2 | 1 | 1.172716857 | 5.03855938 | 0.295715105 | 0.004 | 0.612 | |
| Hot_control.2_Antibiotics vs Hot_control.4_Transplant2 | 1 | 1.204981537 | 5.30457458 | 0.289795021 | 0.001 | 0.153 | |
| Hot_control.2_Antibiotics vs Control.5_Post-FMT1 | 1 | 1.680870091 | 6.19302659 | 0.292220017 | 0.001 | 0.153 | |
| Hot_control.2_Antibiotics vs Treatment.5_Post-FMT1 | 1 | 1.405394667 | 5.16721122 | 0.269585969 | 0.001 | 0.153 | |
| Hot_control.2_Antibiotics vs Hot_control.5_Post-FMT1 | 1 | 1.362143588 | 5.32040618 | 0.261825779 | 0.001 | 0.153 | |
| Hot_control.2_Antibiotics vs Treatment.6_Post-FMT2 | 1 | 1.579851347 | 6.56137424 | 0.304311505 | 0.001 | 0.153 | |
| Hot_control.2_Antibiotics vs Control.6_Post-FMT2 | 1 | 1.877053661 | 7.49890802 | 0.333300977 | 0.001 | 0.153 | |
| Hot_control.2_Antibiotics vs Hot_control.6_Post-FMT2 | 1 | 1.274827637 | 4.94469852 | 0.247920444 | 0.001 | 0.153 | |
| Control.3_Transplant1 vs Treatment.3_Transplant1 | 1 | 1.210492327 | 6.33244693 | 0.311445393 | 0.001 | 0.153 | |
| Control.3_Transplant1 vs Hot_control.3_Transplant1 | 1 | 1.329816668 | 7.07170798 | 0.320396953 | 0.001 | 0.153 | |
| Control.3_Transplant1 vs Control.4_Transplant2 | 1 | 0.095527891 | 0.51782131 | 0.031349250 | 0.906 | 1.000 | |
| Control.3_Transplant1 vs Treatment.4_Transplant2 | 1 | 1.202957055 | 6.54398303 | 0.334833643 | 0.001 | 0.153 | |
| Control.3_Transplant1 vs Hot_control.4_Transplant2 | 1 | 1.336430279 | 7.33775774 | 0.343886074 | 0.001 | 0.153 | |
| Control.3_Transplant1 vs Control.5_Post-FMT1 | 1 | 0.869030126 | 3.79079171 | 0.191543207 | 0.001 | 0.153 | |
| Control.3_Transplant1 vs Treatment.5_Post-FMT1 | 1 | 1.126784833 | 4.96449923 | 0.248666354 | 0.001 | 0.153 | |
| Control.3_Transplant1 vs Hot_control.5_Post-FMT1 | 1 | 1.449934099 | 6.74957054 | 0.296690020 | 0.001 | 0.153 | |
| Control.3_Transplant1 vs Treatment.6_Post-FMT2 | 1 | 1.104217737 | 5.50651250 | 0.256039304 | 0.001 | 0.153 | |
| Control.3_Transplant1 vs Control.6_Post-FMT2 | 1 | 0.789637714 | 3.76981024 | 0.190685201 | 0.001 | 0.153 | |
| Control.3_Transplant1 vs Hot_control.6_Post-FMT2 | 1 | 1.536951488 | 7.09904578 | 0.307330694 | 0.001 | 0.153 | |
| Treatment.3_Transplant1 vs Hot_control.3_Transplant1 | 1 | 0.009634139 | 0.05096559 | 0.003905121 | 1.000 | 1.000 | |
| Treatment.3_Transplant1 vs Control.4_Transplant2 | 1 | 1.400930696 | 7.57729789 | 0.351169916 | 0.002 | 0.306 | |
| Treatment.3_Transplant1 vs Treatment.4_Transplant2 | 1 | 0.070676505 | 0.38364693 | 0.033701584 | 0.921 | 1.000 | |
| Treatment.3_Transplant1 vs Hot_control.4_Transplant2 | 1 | 0.078300724 | 0.42972488 | 0.034572356 | 0.887 | 1.000 | |
| Treatment.3_Transplant1 vs Control.5_Post-FMT1 | 1 | 1.367122874 | 5.79171991 | 0.292633482 | 0.001 | 0.153 | |
| Treatment.3_Transplant1 vs Treatment.5_Post-FMT1 | 1 | 0.751062093 | 3.21047535 | 0.198049426 | 0.001 | 0.153 | |
| Treatment.3_Transplant1 vs Hot_control.5_Post-FMT1 | 1 | 0.541926060 | 2.46826366 | 0.149880019 | 0.002 | 0.306 | |
| Treatment.3_Transplant1 vs Treatment.6_Post-FMT2 | 1 | 0.592624022 | 2.91606971 | 0.172384588 | 0.001 | 0.153 | |
| Treatment.3_Transplant1 vs Control.6_Post-FMT2 | 1 | 1.523500334 | 7.13792666 | 0.337683387 | 0.001 | 0.153 | |
| Treatment.3_Transplant1 vs Hot_control.6_Post-FMT2 | 1 | 0.502178159 | 2.26737096 | 0.139381524 | 0.011 | 1.000 | |
| Hot_control.3_Transplant1 vs Control.4_Transplant2 | 1 | 1.529217161 | 8.39335845 | 0.358792367 | 0.001 | 0.153 | |
| Hot_control.3_Transplant1 vs Treatment.4_Transplant2 | 1 | 0.067325622 | 0.37214249 | 0.030079066 | 0.934 | 1.000 | |
| Hot_control.3_Transplant1 vs Hot_control.4_Transplant2 | 1 | 0.060700684 | 0.33852115 | 0.025379211 | 0.937 | 1.000 | |
| Hot_control.3_Transplant1 vs Control.5_Post-FMT1 | 1 | 1.488235610 | 6.47212491 | 0.301419861 | 0.001 | 0.153 | |
| Hot_control.3_Transplant1 vs Treatment.5_Post-FMT1 | 1 | 0.831659790 | 3.65479114 | 0.207014125 | 0.003 | 0.459 | |
| Hot_control.3_Transplant1 vs Hot_control.5_Post-FMT1 | 1 | 0.615759083 | 2.86994158 | 0.160601621 | 0.002 | 0.306 | |
| Hot_control.3_Transplant1 vs Treatment.6_Post-FMT2 | 1 | 0.650193072 | 3.26217668 | 0.178630222 | 0.001 | 0.153 | |
| Hot_control.3_Transplant1 vs Control.6_Post-FMT2 | 1 | 1.644765070 | 7.87562973 | 0.344280347 | 0.001 | 0.153 | |
| Hot_control.3_Transplant1 vs Hot_control.6_Post-FMT2 | 1 | 0.529144591 | 2.44579001 | 0.140193709 | 0.004 | 0.612 | |
| Control.4_Transplant2 vs Treatment.4_Transplant2 | 1 | 1.318966989 | 7.44875581 | 0.364264500 | 0.001 | 0.153 | |
| Control.4_Transplant2 vs Hot_control.4_Transplant2 | 1 | 1.461842335 | 8.31259383 | 0.372551658 | 0.001 | 0.153 | |
| Control.4_Transplant2 vs Control.5_Post-FMT1 | 1 | 0.837927278 | 3.74476269 | 0.189658531 | 0.001 | 0.153 | |
| Control.4_Transplant2 vs Treatment.5_Post-FMT1 | 1 | 1.237187346 | 5.59522781 | 0.271675937 | 0.001 | 0.153 | |
| Control.4_Transplant2 vs Hot_control.5_Post-FMT1 | 1 | 1.592357282 | 7.60689288 | 0.322231855 | 0.001 | 0.153 | |
| Control.4_Transplant2 vs Treatment.6_Post-FMT2 | 1 | 1.205873137 | 6.18264781 | 0.278715502 | 0.001 | 0.153 | |
| Control.4_Transplant2 vs Control.6_Post-FMT2 | 1 | 0.632526126 | 3.10098856 | 0.162347020 | 0.001 | 0.153 | |
| Control.4_Transplant2 vs Hot_control.6_Post-FMT2 | 1 | 1.582795172 | 7.50092755 | 0.319175808 | 0.001 | 0.153 | |
| Treatment.4_Transplant2 vs Hot_control.4_Transplant2 | 1 | 0.012314222 | 0.07128989 | 0.006439167 | 1.000 | 1.000 | |
| Treatment.4_Transplant2 vs Control.5_Post-FMT1 | 1 | 1.342607528 | 5.78285738 | 0.307879534 | 0.001 | 0.153 | |
| Treatment.4_Transplant2 vs Treatment.5_Post-FMT1 | 1 | 0.747258352 | 3.25510805 | 0.213378236 | 0.001 | 0.153 | |
| Treatment.4_Transplant2 vs Hot_control.5_Post-FMT1 | 1 | 0.553729649 | 2.58255490 | 0.165733727 | 0.005 | 0.765 | |
| Treatment.4_Transplant2 vs Treatment.6_Post-FMT2 | 1 | 0.566094860 | 2.87613678 | 0.181160998 | 0.001 | 0.153 | |
| Treatment.4_Transplant2 vs Control.6_Post-FMT2 | 1 | 1.445460896 | 6.95533179 | 0.348545034 | 0.001 | 0.153 | |
| Treatment.4_Transplant2 vs Hot_control.6_Post-FMT2 | 1 | 0.460097406 | 2.12533559 | 0.140514938 | 0.006 | 0.918 | |
| Hot_control.4_Transplant2 vs Control.5_Post-FMT1 | 1 | 1.463793289 | 6.44782331 | 0.315330547 | 0.001 | 0.153 | |
| Hot_control.4_Transplant2 vs Treatment.5_Post-FMT1 | 1 | 0.828827179 | 3.69648712 | 0.221393105 | 0.003 | 0.459 | |
| Hot_control.4_Transplant2 vs Hot_control.5_Post-FMT1 | 1 | 0.627968770 | 2.98278398 | 0.175635749 | 0.002 | 0.306 | |
| Hot_control.4_Transplant2 vs Treatment.6_Post-FMT2 | 1 | 0.613502889 | 3.15912171 | 0.184107425 | 0.001 | 0.153 | |
| Hot_control.4_Transplant2 vs Control.6_Post-FMT2 | 1 | 1.570619682 | 7.68364100 | 0.354351974 | 0.001 | 0.153 | |
| Hot_control.4_Transplant2 vs Hot_control.6_Post-FMT2 | 1 | 0.469820686 | 2.21140107 | 0.136410238 | 0.007 | 1.000 | |
| Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 | 1 | 0.605177838 | 2.25084909 | 0.130477583 | 0.015 | 1.000 | |
| Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 1.052890187 | 4.14363688 | 0.205704506 | 0.001 | 0.153 | |
| Control.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.890815765 | 3.71469204 | 0.188422524 | 0.001 | 0.153 | |
| Control.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 0.386092689 | 1.55217583 | 0.088432104 | 0.078 | 1.000 | |
| Control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 1.312223689 | 5.13027257 | 0.242792541 | 0.001 | 0.153 | |
| Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.415007562 | 1.63726831 | 0.098409684 | 0.054 | 1.000 | |
| Treatment.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.315707871 | 1.32520255 | 0.081175259 | 0.167 | 1.000 | |
| Treatment.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 1.057952050 | 4.27000973 | 0.221588354 | 0.001 | 0.153 | |
| Treatment.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 0.745401493 | 2.92004928 | 0.162948730 | 0.001 | 0.153 | |
| Hot_control.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.437716066 | 1.94212609 | 0.108243922 | 0.008 | 1.000 | |
| Hot_control.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 1.376659749 | 5.87527887 | 0.268580753 | 0.001 | 0.153 | |
| Hot_control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 0.317651641 | 1.31613667 | 0.076006369 | 0.163 | 1.000 | |
| Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 | 1 | 1.022748131 | 4.64833456 | 0.225119103 | 0.001 | 0.153 | |
| Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.501020156 | 2.20653207 | 0.121194529 | 0.001 | 0.153 | |
| Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 1.361942386 | 5.77103132 | 0.265078454 | 0.001 | 0.153 |
4.3.2.12.3 Phylogenetic
betadisper(beta_div_phylo_all_comparison$S, all_comparison_arrange$time_point) %>% permutest(., pairwise = TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 5 1.4944 0.298874 25.319 999 0.001 ***
Residuals 143 1.6880 0.011804
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
1_Acclimation 2_Antibiotics 3_Transplant1 4_Transplant2 5_Post-FMT1 6_Post-FMT2
1_Acclimation 1.0000e-03 1.9400e-01 3.0000e-02 1.0800e-01 0.027
2_Antibiotics 3.2520e-07 1.0000e-03 1.0000e-03 1.0000e-03 0.001
3_Transplant1 1.7307e-01 7.3009e-11 4.8300e-01 8.2000e-01 0.569
4_Transplant2 3.3595e-02 6.1072e-17 4.8835e-01 6.2800e-01 0.847
5_Post-FMT1 1.1014e-01 7.7616e-12 8.3839e-01 6.4333e-01 0.718
6_Post-FMT2 3.0349e-02 1.9424e-17 5.4602e-01 8.4889e-01 7.1872e-01
#adonis2(formula=beta_div_phylo_all_comparison$S ~ time_point*type, data=all_comparison_arrange[labels(beta_div_phylo_all_comparison$S),], permutations=999,strata=all_comparison_arrange$individual) %>%
#as.matrix() %>%
#kable()pairwise <- pairwise.adonis(beta_div_phylo_all_comparison$S, all_comparison_arrange$type_time, perm=999)
pairwise%>%
tt()| pairs | Df | SumsOfSqs | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|---|
| Control.1_Acclimation vs Treatment.1_Acclimation | 1 | 0.041869229 | 0.43916424 | 0.026714511 | 0.766 | 1.000 | |
| Control.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.156094158 | 2.55468892 | 0.137684276 | 0.038 | 1.000 | |
| Control.1_Acclimation vs Control.2_Antibiotics | 1 | 0.861337080 | 5.65998778 | 0.273958912 | 0.001 | 0.153 | |
| Control.1_Acclimation vs Treatment.2_Antibiotics | 1 | 0.913173267 | 6.56821615 | 0.319338153 | 0.003 | 0.459 | |
| Control.1_Acclimation vs Hot_control.2_Antibiotics | 1 | 0.700066804 | 5.49233852 | 0.268019119 | 0.001 | 0.153 | |
| Control.1_Acclimation vs Control.3_Transplant1 | 1 | 0.033988546 | 0.51289646 | 0.031060357 | 0.762 | 1.000 | |
| Control.1_Acclimation vs Treatment.3_Transplant1 | 1 | 0.215372330 | 2.72760052 | 0.163059879 | 0.031 | 1.000 | |
| Control.1_Acclimation vs Hot_control.3_Transplant1 | 1 | 0.231242466 | 3.05823713 | 0.169354135 | 0.015 | 1.000 | |
| Control.1_Acclimation vs Control.4_Transplant2 | 1 | 0.028305397 | 0.51176469 | 0.030993943 | 0.685 | 1.000 | |
| Control.1_Acclimation vs Treatment.4_Transplant2 | 1 | 0.208435351 | 2.91338090 | 0.183077431 | 0.027 | 1.000 | |
| Control.1_Acclimation vs Hot_control.4_Transplant2 | 1 | 0.238647956 | 3.50464793 | 0.200212420 | 0.006 | 0.918 | |
| Control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.191933673 | 2.97499219 | 0.156784896 | 0.027 | 1.000 | |
| Control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.146272881 | 1.79073807 | 0.106650349 | 0.137 | 1.000 | |
| Control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.250613478 | 3.61461852 | 0.184281867 | 0.002 | 0.306 | |
| Control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.263223310 | 4.30602808 | 0.212056640 | 0.007 | 1.000 | |
| Control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.160478949 | 2.54057422 | 0.137027807 | 0.034 | 1.000 | |
| Control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.255295098 | 4.01091382 | 0.200436315 | 0.001 | 0.153 | |
| Treatment.1_Acclimation vs Hot_control.1_Acclimation | 1 | 0.231088456 | 4.05218385 | 0.202081922 | 0.003 | 0.459 | |
| Treatment.1_Acclimation vs Control.2_Antibiotics | 1 | 0.696872587 | 4.71383535 | 0.239113053 | 0.002 | 0.306 | |
| Treatment.1_Acclimation vs Treatment.2_Antibiotics | 1 | 0.689137599 | 5.12849058 | 0.268107437 | 0.003 | 0.459 | |
| Treatment.1_Acclimation vs Hot_control.2_Antibiotics | 1 | 0.596592947 | 4.84570105 | 0.244168802 | 0.001 | 0.153 | |
| Treatment.1_Acclimation vs Control.3_Transplant1 | 1 | 0.105837772 | 1.70171045 | 0.096132543 | 0.148 | 1.000 | |
| Treatment.1_Acclimation vs Treatment.3_Transplant1 | 1 | 0.271525858 | 3.65417744 | 0.206986559 | 0.004 | 0.612 | |
| Treatment.1_Acclimation vs Hot_control.3_Transplant1 | 1 | 0.286283719 | 4.01697126 | 0.211230864 | 0.002 | 0.306 | |
| Treatment.1_Acclimation vs Control.4_Transplant2 | 1 | 0.075042716 | 1.46463442 | 0.083862873 | 0.253 | 1.000 | |
| Treatment.1_Acclimation vs Treatment.4_Transplant2 | 1 | 0.276043921 | 4.14908181 | 0.241941922 | 0.020 | 1.000 | |
| Treatment.1_Acclimation vs Hot_control.4_Transplant2 | 1 | 0.308698871 | 4.86600187 | 0.257924382 | 0.002 | 0.306 | |
| Treatment.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.263584652 | 4.36089602 | 0.214179966 | 0.006 | 0.918 | |
| Treatment.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.253194274 | 3.27384223 | 0.179154563 | 0.039 | 1.000 | |
| Treatment.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.390501202 | 5.98373926 | 0.272189330 | 0.001 | 0.153 | |
| Treatment.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.364968924 | 6.39666658 | 0.285607975 | 0.001 | 0.153 | |
| Treatment.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.226282101 | 3.82922197 | 0.193110046 | 0.022 | 1.000 | |
| Treatment.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.348308136 | 5.84633352 | 0.267611657 | 0.001 | 0.153 | |
| Hot_control.1_Acclimation vs Control.2_Antibiotics | 1 | 1.097240771 | 9.85701700 | 0.396548669 | 0.001 | 0.153 | |
| Hot_control.1_Acclimation vs Treatment.2_Antibiotics | 1 | 1.223589280 | 12.84663319 | 0.478519340 | 0.001 | 0.153 | |
| Hot_control.1_Acclimation vs Hot_control.2_Antibiotics | 1 | 0.661505415 | 7.63879773 | 0.337420645 | 0.001 | 0.153 | |
| Hot_control.1_Acclimation vs Control.3_Transplant1 | 1 | 0.081539620 | 2.91655101 | 0.154179851 | 0.002 | 0.306 | |
| Hot_control.1_Acclimation vs Treatment.3_Transplant1 | 1 | 0.030226267 | 0.85925828 | 0.057826458 | 0.552 | 1.000 | |
| Hot_control.1_Acclimation vs Hot_control.3_Transplant1 | 1 | 0.033410268 | 0.96148331 | 0.060237716 | 0.491 | 1.000 | |
| Hot_control.1_Acclimation vs Control.4_Transplant2 | 1 | 0.096380142 | 5.66972138 | 0.261642560 | 0.001 | 0.153 | |
| Hot_control.1_Acclimation vs Treatment.4_Transplant2 | 1 | 0.035245258 | 1.44489196 | 0.100027883 | 0.201 | 1.000 | |
| Hot_control.1_Acclimation vs Hot_control.4_Transplant2 | 1 | 0.032336674 | 1.33009788 | 0.086763822 | 0.241 | 1.000 | |
| Hot_control.1_Acclimation vs Control.5_Post-FMT1 | 1 | 0.142033756 | 5.42002118 | 0.253035286 | 0.001 | 0.153 | |
| Hot_control.1_Acclimation vs Treatment.5_Post-FMT1 | 1 | 0.096667531 | 2.36821735 | 0.136353507 | 0.020 | 1.000 | |
| Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1 | 1 | 0.092525999 | 2.98249577 | 0.157118211 | 0.009 | 1.000 | |
| Hot_control.1_Acclimation vs Treatment.6_Post-FMT2 | 1 | 0.100028707 | 4.38362373 | 0.215056154 | 0.001 | 0.153 | |
| Hot_control.1_Acclimation vs Control.6_Post-FMT2 | 1 | 0.125775098 | 5.06012870 | 0.240270550 | 0.001 | 0.153 | |
| Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2 | 1 | 0.063343780 | 2.49977367 | 0.135124554 | 0.022 | 1.000 | |
| Control.2_Antibiotics vs Treatment.2_Antibiotics | 1 | 0.094083678 | 0.46354958 | 0.034429968 | 0.858 | 1.000 | |
| Control.2_Antibiotics vs Hot_control.2_Antibiotics | 1 | 0.631160740 | 3.39326529 | 0.195090757 | 0.001 | 0.153 | |
| Control.2_Antibiotics vs Control.3_Transplant1 | 1 | 1.081539517 | 9.25761888 | 0.381637576 | 0.001 | 0.153 | |
| Control.2_Antibiotics vs Treatment.3_Transplant1 | 1 | 1.004005224 | 7.26097560 | 0.358372457 | 0.001 | 0.153 | |
| Control.2_Antibiotics vs Hot_control.3_Transplant1 | 1 | 1.080757579 | 8.28477913 | 0.371768510 | 0.001 | 0.153 | |
| Control.2_Antibiotics vs Control.4_Transplant2 | 1 | 1.152040544 | 10.95741891 | 0.422130526 | 0.001 | 0.153 | |
| Control.2_Antibiotics vs Treatment.4_Transplant2 | 1 | 1.027702743 | 7.60232276 | 0.387827650 | 0.001 | 0.153 | |
| Control.2_Antibiotics vs Hot_control.4_Transplant2 | 1 | 1.079920015 | 8.53201595 | 0.396247893 | 0.001 | 0.153 | |
| Control.2_Antibiotics vs Control.5_Post-FMT1 | 1 | 1.083638513 | 9.42638693 | 0.385909998 | 0.001 | 0.153 | |
| Control.2_Antibiotics vs Treatment.5_Post-FMT1 | 1 | 1.014079699 | 7.40449675 | 0.345931831 | 0.003 | 0.459 | |
| Control.2_Antibiotics vs Hot_control.5_Post-FMT1 | 1 | 1.241652163 | 10.33876224 | 0.408021597 | 0.001 | 0.153 | |
| Control.2_Antibiotics vs Treatment.6_Post-FMT2 | 1 | 1.183002877 | 10.62460826 | 0.414625197 | 0.001 | 0.153 | |
| Control.2_Antibiotics vs Control.6_Post-FMT2 | 1 | 1.135907392 | 10.00634083 | 0.400152141 | 0.001 | 0.153 | |
| Control.2_Antibiotics vs Hot_control.6_Post-FMT2 | 1 | 1.225141484 | 10.74358446 | 0.417330558 | 0.001 | 0.153 | |
| Treatment.2_Antibiotics vs Hot_control.2_Antibiotics | 1 | 0.751305380 | 4.30687517 | 0.248853426 | 0.002 | 0.306 | |
| Treatment.2_Antibiotics vs Control.3_Transplant1 | 1 | 1.147414607 | 11.34359994 | 0.447592290 | 0.001 | 0.153 | |
| Treatment.2_Antibiotics vs Treatment.3_Transplant1 | 1 | 1.144190161 | 9.39612190 | 0.439150700 | 0.004 | 0.612 | |
| Treatment.2_Antibiotics vs Hot_control.3_Transplant1 | 1 | 1.209231054 | 10.55018954 | 0.447987458 | 0.001 | 0.153 | |
| Treatment.2_Antibiotics vs Control.4_Transplant2 | 1 | 1.175774920 | 13.26657109 | 0.486550767 | 0.001 | 0.153 | |
| Treatment.2_Antibiotics vs Treatment.4_Transplant2 | 1 | 1.099595076 | 9.40628827 | 0.460950475 | 0.003 | 0.459 | |
| Treatment.2_Antibiotics vs Hot_control.4_Transplant2 | 1 | 1.178403909 | 10.80153268 | 0.473719588 | 0.003 | 0.459 | |
| Treatment.2_Antibiotics vs Control.5_Post-FMT1 | 1 | 1.144139048 | 11.53966642 | 0.451833091 | 0.002 | 0.306 | |
| Treatment.2_Antibiotics vs Treatment.5_Post-FMT1 | 1 | 1.099646668 | 9.04159547 | 0.410206034 | 0.003 | 0.459 | |
| Treatment.2_Antibiotics vs Hot_control.5_Post-FMT1 | 1 | 1.342895837 | 12.83174099 | 0.478229907 | 0.001 | 0.153 | |
| Treatment.2_Antibiotics vs Treatment.6_Post-FMT2 | 1 | 1.284614156 | 13.48281445 | 0.490590746 | 0.001 | 0.153 | |
| Treatment.2_Antibiotics vs Control.6_Post-FMT2 | 1 | 1.215005290 | 12.44801755 | 0.470659758 | 0.001 | 0.153 | |
| Treatment.2_Antibiotics vs Hot_control.6_Post-FMT2 | 1 | 1.268089884 | 12.91871587 | 0.479915756 | 0.002 | 0.306 | |
| Hot_control.2_Antibiotics vs Control.3_Transplant1 | 1 | 0.849076966 | 9.21813905 | 0.380629537 | 0.001 | 0.153 | |
| Hot_control.2_Antibiotics vs Treatment.3_Transplant1 | 1 | 0.596636472 | 5.43613272 | 0.294862963 | 0.001 | 0.153 | |
| Hot_control.2_Antibiotics vs Hot_control.3_Transplant1 | 1 | 0.615692862 | 5.92195472 | 0.297257714 | 0.001 | 0.153 | |
| Hot_control.2_Antibiotics vs Control.4_Transplant2 | 1 | 0.876797436 | 10.90268092 | 0.420909363 | 0.001 | 0.153 | |
| Hot_control.2_Antibiotics vs Treatment.4_Transplant2 | 1 | 0.674952556 | 6.47214657 | 0.350373279 | 0.001 | 0.153 | |
| Hot_control.2_Antibiotics vs Hot_control.4_Transplant2 | 1 | 0.686432726 | 7.00067496 | 0.350021935 | 0.003 | 0.459 | |
| Hot_control.2_Antibiotics vs Control.5_Post-FMT1 | 1 | 0.849330865 | 9.41186993 | 0.385544817 | 0.001 | 0.153 | |
| Hot_control.2_Antibiotics vs Treatment.5_Post-FMT1 | 1 | 0.752694639 | 6.81347752 | 0.327358920 | 0.001 | 0.153 | |
| Hot_control.2_Antibiotics vs Hot_control.5_Post-FMT1 | 1 | 0.843320169 | 8.84176238 | 0.370851879 | 0.001 | 0.153 | |
| Hot_control.2_Antibiotics vs Treatment.6_Post-FMT2 | 1 | 0.983612960 | 11.35445189 | 0.430836199 | 0.001 | 0.153 | |
| Hot_control.2_Antibiotics vs Control.6_Post-FMT2 | 1 | 0.885529745 | 9.97205129 | 0.399328480 | 0.001 | 0.153 | |
| Hot_control.2_Antibiotics vs Hot_control.6_Post-FMT2 | 1 | 0.840123658 | 9.40607766 | 0.385398989 | 0.001 | 0.153 | |
| Control.3_Transplant1 vs Treatment.3_Transplant1 | 1 | 0.098602035 | 2.40012240 | 0.146347835 | 0.041 | 1.000 | |
| Control.3_Transplant1 vs Hot_control.3_Transplant1 | 1 | 0.111980145 | 2.78142890 | 0.156423250 | 0.018 | 1.000 | |
| Control.3_Transplant1 vs Control.4_Transplant2 | 1 | 0.007928377 | 0.35768341 | 0.021866385 | 0.943 | 1.000 | |
| Control.3_Transplant1 vs Treatment.4_Transplant2 | 1 | 0.107460204 | 3.49439587 | 0.211853523 | 0.015 | 1.000 | |
| Control.3_Transplant1 vs Hot_control.4_Transplant2 | 1 | 0.123295950 | 4.08042827 | 0.225682058 | 0.004 | 0.612 | |
| Control.3_Transplant1 vs Control.5_Post-FMT1 | 1 | 0.096104530 | 3.06336633 | 0.160693881 | 0.001 | 0.153 | |
| Control.3_Transplant1 vs Treatment.5_Post-FMT1 | 1 | 0.082810318 | 1.78740344 | 0.106472895 | 0.157 | 1.000 | |
| Control.3_Transplant1 vs Hot_control.5_Post-FMT1 | 1 | 0.118218149 | 3.26661338 | 0.169547876 | 0.006 | 0.918 | |
| Control.3_Transplant1 vs Treatment.6_Post-FMT2 | 1 | 0.136974123 | 4.89446159 | 0.234246840 | 0.001 | 0.153 | |
| Control.3_Transplant1 vs Control.6_Post-FMT2 | 1 | 0.097169276 | 3.23650370 | 0.168248022 | 0.004 | 0.612 | |
| Control.3_Transplant1 vs Hot_control.6_Post-FMT2 | 1 | 0.152527104 | 4.99980434 | 0.238088139 | 0.001 | 0.153 | |
| Treatment.3_Transplant1 vs Hot_control.3_Transplant1 | 1 | 0.001916654 | 0.03838869 | 0.002944282 | 0.975 | 1.000 | |
| Treatment.3_Transplant1 vs Control.4_Transplant2 | 1 | 0.138394849 | 4.84607372 | 0.257139699 | 0.001 | 0.153 | |
| Treatment.3_Transplant1 vs Treatment.4_Transplant2 | 1 | 0.022693131 | 0.56103262 | 0.048527899 | 0.638 | 1.000 | |
| Treatment.3_Transplant1 vs Hot_control.4_Transplant2 | 1 | 0.016177953 | 0.41465150 | 0.033400172 | 0.755 | 1.000 | |
| Treatment.3_Transplant1 vs Control.5_Post-FMT1 | 1 | 0.114864381 | 2.93924035 | 0.173516656 | 0.007 | 1.000 | |
| Treatment.3_Transplant1 vs Treatment.5_Post-FMT1 | 1 | 0.083463697 | 1.46603865 | 0.101343477 | 0.171 | 1.000 | |
| Treatment.3_Transplant1 vs Hot_control.5_Post-FMT1 | 1 | 0.064149535 | 1.43879911 | 0.093193719 | 0.308 | 1.000 | |
| Treatment.3_Transplant1 vs Treatment.6_Post-FMT2 | 1 | 0.100463445 | 2.85333394 | 0.169303827 | 0.004 | 0.612 | |
| Treatment.3_Transplant1 vs Control.6_Post-FMT2 | 1 | 0.132425349 | 3.52780737 | 0.201269177 | 0.001 | 0.153 | |
| Treatment.3_Transplant1 vs Hot_control.6_Post-FMT2 | 1 | 0.069766618 | 1.83160737 | 0.115693077 | 0.125 | 1.000 | |
| Hot_control.3_Transplant1 vs Control.4_Transplant2 | 1 | 0.151529980 | 5.30364409 | 0.261216364 | 0.001 | 0.153 | |
| Hot_control.3_Transplant1 vs Treatment.4_Transplant2 | 1 | 0.018543667 | 0.46976985 | 0.037672696 | 0.701 | 1.000 | |
| Hot_control.3_Transplant1 vs Hot_control.4_Transplant2 | 1 | 0.011324236 | 0.29624361 | 0.022280248 | 0.835 | 1.000 | |
| Hot_control.3_Transplant1 vs Control.5_Post-FMT1 | 1 | 0.129157334 | 3.36426411 | 0.183196239 | 0.004 | 0.612 | |
| Hot_control.3_Transplant1 vs Treatment.5_Post-FMT1 | 1 | 0.096537959 | 1.75784138 | 0.111553438 | 0.077 | 1.000 | |
| Hot_control.3_Transplant1 vs Hot_control.5_Post-FMT1 | 1 | 0.077813129 | 1.78758462 | 0.106482538 | 0.195 | 1.000 | |
| Hot_control.3_Transplant1 vs Treatment.6_Post-FMT2 | 1 | 0.115011676 | 3.30697422 | 0.180640131 | 0.003 | 0.459 | |
| Hot_control.3_Transplant1 vs Control.6_Post-FMT2 | 1 | 0.146133526 | 3.95471424 | 0.208640140 | 0.001 | 0.153 | |
| Hot_control.3_Transplant1 vs Hot_control.6_Post-FMT2 | 1 | 0.066529056 | 1.77563878 | 0.105846270 | 0.118 | 1.000 | |
| Control.4_Transplant2 vs Treatment.4_Transplant2 | 1 | 0.123072115 | 7.12848761 | 0.354149191 | 0.001 | 0.153 | |
| Control.4_Transplant2 vs Hot_control.4_Transplant2 | 1 | 0.149801593 | 8.46696532 | 0.376862883 | 0.001 | 0.153 | |
| Control.4_Transplant2 vs Control.5_Post-FMT1 | 1 | 0.113967223 | 5.58286749 | 0.258671258 | 0.001 | 0.153 | |
| Control.4_Transplant2 vs Treatment.5_Post-FMT1 | 1 | 0.106396986 | 3.07142348 | 0.169960240 | 0.018 | 1.000 | |
| Control.4_Transplant2 vs Hot_control.5_Post-FMT1 | 1 | 0.165133142 | 6.54475611 | 0.290300595 | 0.001 | 0.153 | |
| Control.4_Transplant2 vs Treatment.6_Post-FMT2 | 1 | 0.143231082 | 8.41195737 | 0.344583486 | 0.001 | 0.153 | |
| Control.4_Transplant2 vs Control.6_Post-FMT2 | 1 | 0.090154714 | 4.72894088 | 0.228132296 | 0.002 | 0.306 | |
| Control.4_Transplant2 vs Hot_control.6_Post-FMT2 | 1 | 0.168581391 | 8.62389999 | 0.350224781 | 0.001 | 0.153 | |
| Treatment.4_Transplant2 vs Hot_control.4_Transplant2 | 1 | 0.001788609 | 0.06719083 | 0.006071173 | 0.987 | 1.000 | |
| Treatment.4_Transplant2 vs Control.5_Post-FMT1 | 1 | 0.112305287 | 3.92735591 | 0.232012367 | 0.002 | 0.306 | |
| Treatment.4_Transplant2 vs Treatment.5_Post-FMT1 | 1 | 0.091039233 | 1.93447512 | 0.138826551 | 0.078 | 1.000 | |
| Treatment.4_Transplant2 vs Hot_control.5_Post-FMT1 | 1 | 0.066940234 | 1.93888992 | 0.129788085 | 0.107 | 1.000 | |
| Treatment.4_Transplant2 vs Treatment.6_Post-FMT2 | 1 | 0.064927309 | 2.65796523 | 0.169751637 | 0.044 | 1.000 | |
| Treatment.4_Transplant2 vs Control.6_Post-FMT2 | 1 | 0.118518130 | 4.40015430 | 0.252880188 | 0.004 | 0.612 | |
| Treatment.4_Transplant2 vs Hot_control.6_Post-FMT2 | 1 | 0.022954896 | 0.83380428 | 0.060272956 | 0.579 | 1.000 | |
| Hot_control.4_Transplant2 vs Control.5_Post-FMT1 | 1 | 0.131900448 | 4.67500775 | 0.250334983 | 0.003 | 0.459 | |
| Hot_control.4_Transplant2 vs Treatment.5_Post-FMT1 | 1 | 0.104293524 | 2.30585040 | 0.150651570 | 0.033 | 1.000 | |
| Hot_control.4_Transplant2 vs Hot_control.5_Post-FMT1 | 1 | 0.072523344 | 2.15076384 | 0.133167933 | 0.085 | 1.000 | |
| Hot_control.4_Transplant2 vs Treatment.6_Post-FMT2 | 1 | 0.079939737 | 3.28382512 | 0.189994118 | 0.009 | 1.000 | |
| Hot_control.4_Transplant2 vs Control.6_Post-FMT2 | 1 | 0.139482909 | 5.22957940 | 0.271954955 | 0.002 | 0.306 | |
| Hot_control.4_Transplant2 vs Hot_control.6_Post-FMT2 | 1 | 0.027328390 | 1.00380807 | 0.066903553 | 0.426 | 1.000 | |
| Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 | 1 | 0.018425353 | 0.41441625 | 0.026884978 | 0.790 | 1.000 | |
| Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.059879674 | 1.73878474 | 0.098021638 | 0.105 | 1.000 | |
| Control.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.079172437 | 3.01800458 | 0.158691968 | 0.007 | 1.000 | |
| Control.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 0.043354907 | 1.53356039 | 0.087464289 | 0.179 | 1.000 | |
| Control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 0.107830455 | 3.75004375 | 0.189875212 | 0.002 | 0.306 | |
| Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 | 1 | 0.032129657 | 0.64777824 | 0.041397458 | 0.691 | 1.000 | |
| Treatment.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.063935389 | 1.56518170 | 0.094486238 | 0.136 | 1.000 | |
| Treatment.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 0.052659492 | 1.22402031 | 0.075444944 | 0.276 | 1.000 | |
| Treatment.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 0.097535009 | 2.24024291 | 0.129942654 | 0.009 | 1.000 | |
| Hot_control.5_Post-FMT1 vs Treatment.6_Post-FMT2 | 1 | 0.072285454 | 2.32795928 | 0.127016830 | 0.048 | 1.000 | |
| Hot_control.5_Post-FMT1 vs Control.6_Post-FMT2 | 1 | 0.117590942 | 3.55384441 | 0.181746583 | 0.001 | 0.153 | |
| Hot_control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 | 1 | 0.066672554 | 1.98595267 | 0.110416874 | 0.095 | 1.000 | |
| Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 | 1 | 0.059274536 | 2.38202535 | 0.129584488 | 0.030 | 1.000 | |
| Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.069062800 | 2.72246021 | 0.145411456 | 0.003 | 0.459 | |
| Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 | 1 | 0.110817088 | 4.04365610 | 0.201742441 | 0.003 | 0.459 |
beta_richness_nmds_all_comparison <- beta_div_richness_all_comparison$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(all_comparison_nmds, by = join_by(sample == newID))
beta_neutral_nmds_all_comparison <- beta_div_neutral_all_comparison$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(all_comparison_nmds, by = join_by(sample == newID))
beta_phylo_nmds_all_comparison <- beta_div_phylo_all_comparison$S %>%
metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(all_comparison_nmds, by = join_by(sample == newID))